{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "# import packages we need for exploratory data analysis (EDA)\n",
    "import pandas as pd  # to store tabular data\n",
    "import numpy as np  # to do some math\n",
    "import matplotlib.pyplot as plt  # a popular data visualization tool\n",
    "import seaborn as sns  # another popular data visualization tool\n",
    "%matplotlib inline  \n",
    "plt.style.use('fivethirtyeight')  # a popular data visualization theme"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 数据EDA"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>6</th>\n",
       "      <th>148</th>\n",
       "      <th>72</th>\n",
       "      <th>35</th>\n",
       "      <th>0</th>\n",
       "      <th>33.6</th>\n",
       "      <th>0.627</th>\n",
       "      <th>50</th>\n",
       "      <th>1</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>85</td>\n",
       "      <td>66</td>\n",
       "      <td>29</td>\n",
       "      <td>0</td>\n",
       "      <td>26.6</td>\n",
       "      <td>0.351</td>\n",
       "      <td>31</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>8</td>\n",
       "      <td>183</td>\n",
       "      <td>64</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>23.3</td>\n",
       "      <td>0.672</td>\n",
       "      <td>32</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>89</td>\n",
       "      <td>66</td>\n",
       "      <td>23</td>\n",
       "      <td>94</td>\n",
       "      <td>28.1</td>\n",
       "      <td>0.167</td>\n",
       "      <td>21</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0</td>\n",
       "      <td>137</td>\n",
       "      <td>40</td>\n",
       "      <td>35</td>\n",
       "      <td>168</td>\n",
       "      <td>43.1</td>\n",
       "      <td>2.288</td>\n",
       "      <td>33</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>116</td>\n",
       "      <td>74</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>25.6</td>\n",
       "      <td>0.201</td>\n",
       "      <td>30</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   6  148  72  35    0  33.6  0.627  50  1\n",
       "0  1   85  66  29    0  26.6  0.351  31  0\n",
       "1  8  183  64   0    0  23.3  0.672  32  1\n",
       "2  1   89  66  23   94  28.1  0.167  21  0\n",
       "3  0  137  40  35  168  43.1  2.288  33  1\n",
       "4  5  116  74   0    0  25.6  0.201  30  0"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#导入数据\n",
    "pima = pd.read_csv('~/开发/AI相关/特征工程/Feature-Engineering-Made-Easy-master/data/pima.data')\n",
    "pima.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>times_pregnant</th>\n",
       "      <th>plasma_glucose_concentration</th>\n",
       "      <th>diastolic_blood_pressure</th>\n",
       "      <th>triceps_thickness</th>\n",
       "      <th>serum_insulin</th>\n",
       "      <th>bmi</th>\n",
       "      <th>pedigree_function</th>\n",
       "      <th>age</th>\n",
       "      <th>onset_diabetes</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>6</td>\n",
       "      <td>148</td>\n",
       "      <td>72</td>\n",
       "      <td>35</td>\n",
       "      <td>0</td>\n",
       "      <td>33.6</td>\n",
       "      <td>0.627</td>\n",
       "      <td>50</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>85</td>\n",
       "      <td>66</td>\n",
       "      <td>29</td>\n",
       "      <td>0</td>\n",
       "      <td>26.6</td>\n",
       "      <td>0.351</td>\n",
       "      <td>31</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>8</td>\n",
       "      <td>183</td>\n",
       "      <td>64</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>23.3</td>\n",
       "      <td>0.672</td>\n",
       "      <td>32</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>89</td>\n",
       "      <td>66</td>\n",
       "      <td>23</td>\n",
       "      <td>94</td>\n",
       "      <td>28.1</td>\n",
       "      <td>0.167</td>\n",
       "      <td>21</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0</td>\n",
       "      <td>137</td>\n",
       "      <td>40</td>\n",
       "      <td>35</td>\n",
       "      <td>168</td>\n",
       "      <td>43.1</td>\n",
       "      <td>2.288</td>\n",
       "      <td>33</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   times_pregnant  plasma_glucose_concentration  diastolic_blood_pressure  \\\n",
       "0               6                           148                        72   \n",
       "1               1                            85                        66   \n",
       "2               8                           183                        64   \n",
       "3               1                            89                        66   \n",
       "4               0                           137                        40   \n",
       "\n",
       "   triceps_thickness  serum_insulin   bmi  pedigree_function  age  \\\n",
       "0                 35              0  33.6              0.627   50   \n",
       "1                 29              0  26.6              0.351   31   \n",
       "2                  0              0  23.3              0.672   32   \n",
       "3                 23             94  28.1              0.167   21   \n",
       "4                 35            168  43.1              2.288   33   \n",
       "\n",
       "   onset_diabetes  \n",
       "0               1  \n",
       "1               0  \n",
       "2               1  \n",
       "3               0  \n",
       "4               1  "
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#原数据没有列名，手动添加\n",
    "pima_column_names = ['times_pregnant', 'plasma_glucose_concentration', 'diastolic_blood_pressure', 'triceps_thickness', 'serum_insulin', 'bmi', 'pedigree_function', 'age', 'onset_diabetes']\n",
    "pima = pd.read_csv('~/开发/AI相关/特征工程/Feature-Engineering-Made-Easy-master/data/pima.data', names=pima_column_names)\n",
    "pima.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    0.651042\n",
       "1    0.348958\n",
       "Name: onset_diabetes, dtype: float64"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#空准确率，65%的人没有糖尿病\n",
    "#空准确率是指当模型总是预测频率较高的类别时达到的正确率\n",
    "pima['onset_diabetes'].value_counts(normalize=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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j2Wuvvdr8A/DMmTM5+OCD2XHHHfnBD37QWn711Vez++67M2bMGI477jjefvttJk6cSHNzM2PGjOHLX/5yu9O9/fbbHH300YwcOZJRo0ZxwQUXrPT+c9IyM6uwZ555hi996Uvcf//9DB48mBtuuIGjjjqKiRMnct9997H11lu3SRxLlizh9ttv5/TTT+fss88uu8yvfvWrnH322fztb39rd70NDQ385S9/4Z577uHb3/42p59+euu4hx9+mIsvvph77rmH66+/nkceeYSnn36aSZMmcfPNN3PvvffSt29frrnmGiZOnMiAAQO49957ufjii8tO96c//YnHHnuMWbNmMXnyZO677z4OO+ywld53bh40M6uwTTbZhA9+8IMAbLfddjz33HMsWLCAMWPGAPDZz36WCRMmtE5/wAEHALDtttvy/PPPL7e8pqamNvMfcsgh3HrrrctNt2DBAo4++mieffZZJPHWW2+1jhs3bhxDhw4FYP/992fy5Mn069ePqVOnsttuuwHwxhtvsN566y233Lvuumu56YYMGcKBBx7I9OnTOfHEE/nYxz7G7rvv3v2dVcJJy8yswtZcc83W93379u30n3Bbpu/Tpw9LlqRngn71q1/l0UcfZfjw4Vx88cVdWu8ZZ5zB2LFjufLKK5kxYwb7779/67jSf+6VRETwmc98htNOO63D5Zab7o033mCttdbi3nvv5bbbbuPiiy/muuuuW+kmwoo0D0q6VNJsSY+XlH9d0tOSnpD0w0L5KZKm5XEfq0SMZmbVMmjQIAYPHtx6veqqq65i9OjRHc5z4YUXcu+993LttdcyZMgQBg0axOTJkwG49tpry86zYMEChg8fDsDvf//7NuPuvPNO5s2bR3NzM3/+85/Zeeed2XXXXbn++ut59dVXAZg3b15rTa9fv36tNbVy073wwgvMmTOHpUuXMn78eE499VSmTp26IrunjUrVtC4Dfg5c0VIgaTdgPPDBiHgzP3obSVsDhwLbAO8BbpW0RUS8XaFYzcwq7he/+AXHH388r7/+OiNGjODCCy/s1vwXXnghxxxzDGuvvXa7zXDHHnssRx99NBdeeCFjx45tM27nnXfmK1/5Cs8++yyf/OQnW3scfuc73+Gggw5i6dKl9O/fn3POOYeNN96Yww8/nNGjR7Ptttty8cUXLzfdmWeeyRtvvMExxxzT+s/DndXYukIRlXkAsKQRwI0R8f48fA1wUUTcWjLdKQARcVYevhmYGBGTi9P11JOLGxsbaWho6IlF9TrH2jtqPdZlXd7nMnTY0KrF0Z3u3LW6T5uamhg8eHCbspZmrHqwqsZa7ri0qKUnF28BjJX0gKS7JH04l28AvFCYbmYuMzOz1Vw1O2L0A9YFdgY+DFwjaTOg3K1+O6xVrezDxvxgtd7hWHvG3Dn9C+/nVi2OxsZXujl97e3TtdZaq00niBZvvPFGFaJZMatirAsWLGD27Nmtwx3V0quZtGYCkyK1Tz4oaSmwXi7fqDDdhsBLHS1oZZoharUZoxzH2jtqPdaWu2BUu3mwoWHVaB4sbbJaVZvcqq07sQ4aNIiNNtqo8wmpbvPg/wK7A0jaAlgD+A9wA3CopDUlbQo0AA9WLUozM6sZFalpSfoDMA5YT9JM4DTgUuDS3A1+MTAh17qeyJ00ngSWAMe456CZmUGFklZEfKadUZ9rZ/ozgDN6LyIzM6tHviOGmVkVnXXWWQwcOJAFCxYwevRoxo0b1+60Bx10EGeeeWaX79r+6KOP8vLLL7PXXnv1ULTV56RlZqutNa77DX2WLKFfv575Klx80BdXeN5TTz21R2Ioeuyxx5gyZcoqlbR8l3czswo755xz2HHHHRk/fjzTpk0D4Oijj+b6668H4Oyzz2a33XZj5MiRHHvssRRvAnH11Vez1157MXLkSB566CEAXnvtNY455hh22203xo4dy5///GcWL17MWWedxaRJkxgzZgyTJk0qOx3AU0891fpYkVGjRvHMM89UeI90nWtaZmYVNGXKFCZNmsTdd9/NkiVL2HXXXdluu+3aTHPkkUdy0kkntb7/61//yj777APA66+/zi233MLf//53vva1rzF58mTOPfdcdtllFy644ALmz5/PHnvswbhx4zjllFOYMmUKP/rRjwA4/fTTy0536aWXctRRR/HpT3+axYsX8/bbtdv3zUnLzKyC7rvvPvbbbz/WXnttgNZkVHT33Xdz/vnn09zczLx589hqq61apzv44IMBGD16NAsXLmT+/Pncfvvt3HTTTfzsZz8D4M0332TmzJnLLbe96XbaaSfOPfdcXnrpJQ444AA233zzXtn2nuCkZWZWYaWPASl64403+OY3v8kdd9zBhhtuyFlnndXmzhLtPULkiiuuWO4fuv/5z3+2GW5vui233JIdd9yRm2++mU984hOcf/757Lrrriu6eb3K17TMzCpo1KhR3HjjjTQ3N7Nw4UL++te/thnfkqCGDRvGokWLuOGGG9qMv+666wCYPHly6yNN9thjDy666KLWa18tjwAZOHAgCxcubJ23vemmT5/OiBEjOOqoo9hnn3144oknemHLe4ZrWmZmFbTddtvxiU98grFjx7LRRhsxcuTINuOHDBnChAkTGDVqFBtvvPFy3duHDBnCXnvtxcKFC/n5z38OwIknnsgpp5zC6NGjiQg23nhjrr76anbZZRfOO+88xowZw/HHH9/udJMmTeKaa66hX79+rL/++q3X02pRxR5N0tP8aJLa5lh7jh9N0nP8aJLKWRUfTWJmZtYtTlpmZlY3nLTMzKxuOGmZmVndcNIys9VGnz59WLx4cbXDsILFixfTp0/XU5G7vJvZamPgwIEsWrSI5ubm1rIFCxYwaFDXe0ZW06oYa58+fRg4cGCXl+ukZWarDUmss846bcpmz57d5Ue9V5tjrVDzoKRLJc3OTykuHfdNSSFpvTwsSedLmibpUUk7VCJGMzOrfZW6pnUZsHdpoaSNgI8CzxeK9wEa8utI4BcViM/MzOpARZJWRNwNzC0z6ifAt4Di3S3GA1dEcj8wRNLwCoRpZmY1rmq9ByUdCLwYEVNLRm0AvFAYnpnLzMxsNVeVjhiS1gZOBco9A7rcPfs7vM9gY2PjSsWzsvNXkmPtHbUc69w5/QvvyzVYVEZj4yvdnL5292kpx9o7VjTWju5bWa3eg5sDmwJT87NhNgQelrQTqWZV7HKyIfBSRwtbmRtz1uqNPctxrL2j1mMduqg2bpjb0FD/N8wtx7H2jt6KtSrNgxHxWES8KyJGRMQIUqLaISJeBm4AvpB7Ee4MNEXErGrEaWZmtaVSXd7/AEwGtpQ0U9IRHUz+F+BZYBpwMfDVCoRoZmZ1oCLNgxHxmU7Gjyi8D+CY3o7JzMzqj+89aGZmdcNJy8zM6oaTlpmZ1Q0nLTMzqxtOWmZmVjectMzMrG44aZmZWd1w0jIzs7rhJxebteOsRxZUOwQzK+GalpmZ1Q0nLTMzqxtOWmZmVjectMzMrG44aZmZWd1w0jIzs7rhpGVmZnWjUk8uvlTSbEmPF8p+JOlfkh6VdJ2kIYVxp0iaJulpSR+rRIxmZlb7KlXTugzYu6Tsb8D7I+KDwL+BUwAkbQ0cCmyT57lQUt8KxWlmZjWsIkkrIu4G5paU3RIRS/Lg/cCG+f144KqIeDMingOmATtVIk4zM6ttiojKrEgaAdwYEe8vM+7/gKsj4neSfg7cHxG/y+N+DdwUEX8sztPU1NQaeGNjY2+Gbqupi2b0r3YINeXITd6qdgi2mmhoaGh9P3jwYBXHVf3eg5JOBZYAV7YUlZmsw8xa3MDuamxsXKn5K8mx9o72Yh26qLbuPTh3zlyGDhtatfU3NAzq8rSrwvGvRY61yklL0gRgf2CPWFblmwlsVJhsQ+ClSsdmZma1p2pd3iXtDZwEHBgRrxdG3QAcKmlNSZsCDcCD1YjRzMxqS0VqWpL+AIwD1pM0EziN1FtwTeBvkiBdxzoqIp6QdA3wJKnZ8JiIeLsScZqZWW2rSNKKiM+UKf51B9OfAZzRexGZmVk98h0xzMysbjhpmZlZ3XDSMjOzuuGkZWZmdcNJy8zM6oaTlpmZ1Y0uJy1J35C0Xm8GY2Zm1pHu1LT2BKZLulHSIZLW7K2gzMzMyuly0oqIA4FNgJuA44CXJV0iaZfeCs7MzKyoW9e0ImJORFwQESOBXYEPA3dImi7pVEkDeyVKMzMzVqAjhqQ9JP0GuBN4BfgC8Hlge1ItzMzMrFd0+d6Dks4BDgWagCuA70TEi4Xx9wPzejxCMzOzrDs3zF0LOCgi/lFuZES8JWnHngnLzMxsed1JWmcBxedeIWldYEBEvAQQEf/qwdjMzMza6M41rf8lPUW4aEPgup4Lx8zMrH3dSVpbRsRjxYI8/L6eDcnMzKy87iSt2ZLeWyzIw3M6m1HSpZJmS3q8UDZU0t8kNea/6+ZySTpf0jRJj0raoRsxmpnZKqw7SetS4E+S9pe0taQDgD8Cl3Rh3suAvUvKTgZui4gG4LY8DLAP0JBfRwK/6EaMZma2CutOR4wfAG8B5wAbAS+QEtaPO5sxIu6WNKKkeDwwLr+/nPR/Xyfl8isiIoD7JQ2RNDwiZnUjVjMzWwUp5YYKrCglrRsj4v15eH5EDCmMnxcR60q6EfhBRNyby28DToqIfxaX19TU1Bp4Y2NjBbbAVjcXzehf7RBqypGbvFXtEGw10dDQ0Pp+8ODBKo7rTk0LSVsC2wJtbtcUEZeuRHzLraZMWYeZtbiB3dXY2LhS81eSY+0d7cU6dNGCKkTTvrlz5jJ02NCqrb+hYVCXp10Vjn8tcqzduyPGt4H/BqbS9v+1gnS9q7teaWn2kzQcmJ3LZ5KaH1tsCLy0Ass3sx501iNdT+Jz5/TvtaR/yvZdT5626ulOTes4YKeIeLSH1n0DMIF0rWwCcH2h/GuSrgI+AjT5epaZmUH3klYzsEJ3vJD0B1Kni/UkzQROIyWrayQdATwPfCpP/hdgX2AaqUb3xRVZp5mZrXq6k7S+C/xM0kTS3d1bRcTSjmaMiM+0M2qPMtMGcEw34jIzs9VEd5LWZfnvlwplIl3T6ttTAZmZmbWnO0lr016LwszMrAu6nLQiYgaApD7A+u4cYWZmldbl2zjlO1P8HniD1EkCSQdK+n5vBWdmZlbUnXsP/pL01OJNgMW5bDJwSE8HZWZmVk53rmntAbwnP6E4ACLiVUnv6p3QzMzM2upOTasJWK9YIGljwNe2zMysIrqTtC4hPZpkN6CPpJGku7P/slciMzMzK9Gd5sGzSZ0wLgD6k+43+Cvgp70Ql5mZ2XK60+U9gPPyy8zMrOK6c5f33dsbFxG390w4ZmZm7etO8+CvS4bfCaxBepTIZj0WkZmZWTu60zzY5jZOkvoC3wEW9nRQZmZm5XSn92AbEfE2cAbwrZ4Lx8zMrH0rnLSyjwIdPpbEzMysp3SnI8YLpMeQtFgbWAv46soEIOn/kR53EsBjpIc+DgeuAoYCDwOfj4jF7S7EzMxWC93piPG5kuHXgH9HxIIVXbmkDYBvAFtHRLOka4BDSU8u/klEXCXpl8ARwC9WdD1mZrZq6E5HjLt6MYYBkt4i1d5mAbsDn83jLwcm4qRlZrba607z4G9p2zxYVkR8oavLjIgXJZ0DPA80A7cADwHzI2JJnmwmsEFXl2lmZqsupRtddGFC6WfABOD/gBnAxsABpJrQnJbpIuJ/urxyaV3gT6THm8wHrs3Dp0XEe/M0GwF/iYgPFOdtampqDbyxsbGrqzTrsotm9K92CFbGkZu8Ve0QrJc1NDS0vh88eLCK47pzTWsLYL+IuKelQNIY4LsR8bEVjG1P4LmIeDUvbxIwChgiqV+ubW0IvNTRQoob2F2NjY0rNX8lOdbe0V6sQxet8OXaXjF3zlyGDhta7TC6pDdjbWgY1KPLWxXO1VrUW7F2p8v7zsD9JWUPACNXYv3PAztLWluSSM/sehK4A/hknmYCcP1KrMPMzFYR3UlajwBnShoAkP+eAUxZ0ZVHxAPAH0nd2h/L8VwEnAQcL2kaMIzlbyFlZmaroe40Dx4O/B5okjQPWBf4J3DYygQQEacBp5UUPwvstDLLNbaiVDEAABO5SURBVDOzVU93urxPB0bljhHvAWZFxPO9FZiZmVmpbt3GSdIwYBywa0Q8L+k9kjbslcjMzMxKdDlpSdoVeJrUHPjdXNyA/+nXzMwqpDs1rfOAQyJib6DlH38fwNeezMysQrqTtEZExG35fcs/9i6me505zMzMVlh3ktaTkkr/iXhPUld1MzOzXtedWtIJwI2S/ky6we2vSLdxGt8rkZmZmZXock0rIu4HPgg8AVwKPAfsFBH/6KXYzMzM2uhSTUtSX+A24GMR8cPeDcnMzKy8LtW0IuJtYNOuTm9mZtYbupOE/gf4haRNJPWV1Kfl1VvBmZmZFXWnI8Yl+e8XWNblXfl9354MyszMrJxOk5akd0fEy6TmQTMzs6rpSk3r38CgiJgB6UGNEfGJ3g3LzMxseV25HqWS4XG9EIeZmVmnupK0ovNJzMzMel9Xmgf7SdqNZTWu0mEi4vYVDUDSEFInj/eTEuR/ke4mfzUwApgOfDoi5q3oOsys5+w++fedTtPc3MyAAQN6Zf1rTF8TgMUHfbFXlm+1rStJazbpDhgt5pQMB7DZSsTwU+CvEfFJSWsAawPfBm6LiB9IOhk4GThpJdZhZmargE6TVkSM6K2VSxoE7AIcnte1GFgsaTzLrp1dDtyJk5aZ2Wqv2v8YvBnwKvAbSY9IukTSO4D1I2IWQP77rmoGaWZmtUER1etnIWlH4H5gdEQ8IOmnwALg6xExpDDdvIhYtzhvU1NTa+CNjY2VCtlWIxfN6F/tEGrSflOvq+r6PzR4KQAv73pgVeOw3tPQ0ND6fvDgwW16sFf7AY4zgZkR8UAe/iPp+tUrkoZHxCxJw0nX1dpV3MDuamxsXKn5K8mx9o72Yh26aEEVomnf3DlzGTpsaLXD6FIHi97siDFsWOqIsU4PnV+rwrlai3or1qo2D+Y7bbwgactctAfwJHADMCGXTQCur0J4ZmZWY6pd0wL4OnBl7jn4LPBFUjK9RtIRwPPAp6oYn5mZ1YiqJ62ImALsWGbUHpWOxczMalu1ew+amZl1mZOWmZnVjao3D5pZ1+w39bpe65FnVi9c0zIzs7rhpGVmZnXDScvMzOqGr2mZWV1a47rf9Mhy3j1nLms8vuJ3GvEjUirLNS0zM6sbTlpmZlY33DxoZnXlnllv9ujympv7MGBx95Y5dviaPRqDdZ1rWmZmVjectMzMrG44aZmZWd3wNS0zs5XQU13vu6Kj7vmrS9d717TMzKxuOGmZmVndqImkJamvpEck3ZiHN5X0gKRGSVfnpxqbmdlqriaSFnAs8FRh+GzgJxHRAMwDjqhKVGZmVlOqnrQkbQjsB1yShwXsDvwxT3I58PHqRGdmZrWk6kkLOA/4FrA0Dw8D5kfEkjw8E9igGoGZmVltqWqXd0n7A7Mj4iFJ41qKy0waHS2nsbFxpeJY2fkrybH2jnKxzp3TvwqRdKy5ubnaIXTZqhzrnDmv9VIkXVn33LLlL9fg521FvwMaGhraHVft/9MaDRwoaV9gLWAQqeY1RFK/XNvaEHipo4V0tIGdaWxsXKn5K8mx9o72Yh26aEEVounYgAEDqh1ClzQ3N6/SsQ4bVp17D86ZM5dhw8r/n9awx++tcDTltfy/WG99B1S1eTAiTomIDSNiBHAocHtEHAbcAXwyTzYBuL5KIZqZWQ2phWta5ZwEHC9pGuka16+rHI+ZmdWAajcPtoqIO4E78/tngZ2qGY+ZmdWeWq1pmZmZLcdJy8zM6oaTlpmZ1Q0nLTMzqxtOWmZmVjectMzMrG44aZmZWd1w0jIzs7rhpGVmZnXDScvMzOpGzdzGyazFWY9U9u7qc+f0r8k7upvZ8lzTMjOzuuGkZWZmdcNJy8zM6oaTlpmZ1Q0nLTMzqxtVTVqSNpJ0h6SnJD0h6dhcPlTS3yQ15r/rVjNOMzOrDdWuaS0BToiIrYCdgWMkbQ2cDNwWEQ3AbXnYzMxWc1VNWhExKyIezu8XAk8BGwDjgcvzZJcDH69OhGZmVksUEdWOAQBJI4C7gfcDz0fEkMK4eRHRpomwqampNfDGxsYKRWmVcNGM/tUOoSbtN/W6aodg2YcGL612CDXr5V0PXOllNDQ0tL4fPHiwiuNq4o4YkgYCfwKOi4gFkjqbpY3iBnZXY2PjSs1fSatLrJW+O8XcOXMZOmxoRde5ogYMGFDtELqkubl5lY512LA1eymajs2ZM5dhNX6urpM/9731fVXta1pI6k9KWFdGxKRc/Iqk4Xn8cGB2teIzM7PaUe3egwJ+DTwVET8ujLoBmJDfTwCur3RsZmZWe6rdPDga+DzwmKQpuezbwA+AayQdATwPfKpK8ZmZWQ2patKKiHuB9i5g7VHJWMzMrPZV/ZqWmZlZVzlpmZlZ3XDSMjOzulHtjhhmNW33yb+vdgitmqsdgFkNcNIyM+ume2a9WZX1Njf3YcDiZeseO7w6/+RcTW4eNDOzuuGkZWZmdcNJy8zM6oavaZmZ1alqXVsrVclra65pmZlZ3XDSMjOzuuGkZWZmdcNJy8zM6oaTlpmZ1Q33HrRWZz3Sc4+5nzunP0MX9dzyzMzANS0zM6sjNV3TkrQ38FOgL3BJRPygp9dx0YzaqBGcsv2gaodgZlbzajZpSeoLXAB8FJgJ/EPSDRHxZE+uZ7+p1zFgwICeXOSK2f6oakdgZlbzFBHVjqEsSSOBiRHxsTx8CkBEnAXQ1NRUm4GbmVmPGTx4sIrDtXxNawPghcLwzFxmZmarqVpOWipT5tqVmdlqrGavaZFqVhsVhjcEXmoZKK0ympnZqq+Wa1r/ABokbSppDeBQ4IYqx2RmZlVUs0krIpYAXwNuBp4CromIJ3pq+ZL2lvS0pGmSTu6p5fYESRtJukPSU5KekHRsLp8o6UVJU/Jr32rHCiBpuqTHckz/zGVDJf1NUmP+u24NxLllYd9NkbRA0nG1sl8lXSpptqTHC2Vl96OS8/P5+6ikHWog1h9J+leO5zpJQ3L5CEnNhf37yxqItd1jLumUvF+flvSxGoj16kKc0yVNyeVV268dfEf1/vkaEavdi/R/X88AmwFrAFOBrasdVyG+4cAO+f06wL+BrYGJwDerHV+ZeKcD65WU/RA4Ob8/GTi72nGWOQdeBjaplf0K7ALsADze2X4E9gVuIl373Rl4oAZi3Qvol9+fXYh1RHG6GtmvZY95/pxNBdYENs3fE32rGWvJ+HOB/672fu3gO6rXz9earWn1sp2AaRHxbEQsBq4Cxlc5plYRMSsiHs7vF5JqmvXWc3I8cHl+fznw8SrGUs4ewDMRMaPagbSIiLuBuSXF7e3H8cAVkdwPDJE0vDKRlo81Im6J1EICcD/pOnTVtbNf2zMeuCoi3oyI54BppO+LiugoVkkCPg38oVLxtKeD76heP19X16RVN93pJY0AtgceyEVfy9XrS2uhyS0L4BZJD0k6MpetHxGzIJ3gwLuqFl15h9L2w1+L+xXa34+1fg7/F+mXdYtNJT0i6S5JY6sVVIlyx7yW9+tY4JWIaCyUVX2/lnxH9fr5uromrbroTi9pIPAn4LiIWAD8Atgc2A6YRWoqqAWjI2IHYB/gGEm7VDugjih17DkQuDYX1ep+7UjNnsOSTgWWAFfmolnAxhGxPXA88HtJ1b5vWXvHvGb3K/AZ2v7Qqvp+LfMd1e6kZcpWaL+urkmrw+70tUBSf9LJcGVETAKIiFci4u2IWApcTAWbLToSES/lv7OB60hxvdJS/c9/Z1cvwuXsAzwcEa9A7e7XrL39WJPnsKQJwP7AYZEvZuSmtjn5/UOk60RbVC/KDo95re7XfsAngKtbyqq9X8t9R1GB83V1TVo13Z0+t13/GngqIn5cKC+2AR8EPF46b6VJeoekdVreky7GP07anxPyZBOA66sTYVltfrHW4n4taG8/3gB8IffK2hloammWqRalG1yfBBwYEa8Xyt+pdC9RJG0GNADPVifK1pjaO+Y3AIdKWlPSpqRYH6x0fGXsCfwrIma2FFRzv7b3HUUlztdq9DyphRepN8u/Sb9OTq12PCWxjSFVnR8FpuTXvsBvgcdy+Q3A8BqIdTNSb6upwBMt+xIYBtwGNOa/Q6sda45rbWAOMLhQVhP7lZRIZwFvkX6ZHtHefiQ1t1yQz9/HgB1rINZppOsWLefsL/O0B+dzYyrwMHBADcTa7jEHTs379Wlgn2rHmssvA44qmbZq+7WD76heP19r9oa5ZmZmpVbX5kEzM6tDTlpmZlY3nLTMzKxuOGmZmVndcNIyM7O64aRlPU7SOEkzO5+ytuW7aEf+x06rE5Juyv/kbKsgJy0zqwpJd0r60kouY6Kk3xXLImKfiLi8vXmsvjlpmVlNcg3XynHSshWWH0h3iqQnJc2T9BtJa5WZ7mRJz0hamKc9qDDuvfkO1U2S/iPp6sK4kPTV/EC5hZK+J2lzSZOVHuB4Tb4NF5LWlXSjpFdzLDdK6vTRGPlWXnfn5d8q6YLSX+4l27tnYbjNr3xJYyTdJ2m+pBckHZ7LB0u6Isc2Q9J3JPXpwva/T+lBenOVHkj46S5szwBJ5+b1NEm6V9KAPO5ApQf2zc+1nK1Ktu2bSnc9b1J68OBahfHjtezBmc/kWza1bNuvJc1Seqji9wu3Fjo8r/+cfEyek7RPHncG6a7lP5e0SNLPC8f8GEmNpLsqIOmneX8uUHqSwNhcvjfwbeCQvIypuby1BiepT97fM5QerniFpMF5XEvz7wRJz+f9f2pn+9iqrJK3KPFr1XqRHv74OOlGmEOBvwPfB8YBMwvTfQp4D+lH0iHAa+Tb5pBuW3NqHrcWMKYwX5BusTMI2AZ4k3RrmM2AwcCTwIQ87TDSbW3WJj2U7lrgf7uwDZOBc0gPAx0DLAB+l8eNyDH0K2zvnoV5Jxam3RhYSLqvYf8cz3Z53BWke7Ctk5f5b5bdnqfs9gPvIN0S6YtAP9KDAf8DbNPJ9lwA3El67ENfYBTpgYZb5P3+0Rzft0i3XVqjsG0P5uM0lPR8pKPyuJ2Apjxvn7zs9+Vx/wv8Ksf7rryMr+Rxh5NuR/TlHMvRpJukttyJ507gSyXxB/C3HMOAXPa5vD/7ASeQHt65VukxKCyjdbmkR6RMI50zA4FJwG9Lju/FwABgW9I5tlW1P1t+dXCOVzsAv+r3lb/ojioM70u6t9g4CkmrzHxTgPH5/RXARcCGZaYL0mNPWoYfAk4qDJ8LnNfOOrYD5nUS/8akR2isXSj7HSuWtE4Briuzjr75i3DrQtlXgDs72n5Scr+npOxXwGkdbE8foBnYtsy47wLXlEz7IjCusG2fK4z/IcvuHfgr4Cdllrl+3rYBhbLPAHfk94eTHrbaMm7tvD/fnYdbk0vJMd+9k+M2r2Ub6Txp3QZ8tTBuS1Ii7Vc4vhsWxj8IHFrtz5Zf7b/cPGgrq/hgtxmkX+ptSPpCblqaL2k+8H5gvTz6W6SbaT6Ym67+q2T2Vwrvm8sMD8zrWFvSr3Iz0ALgbtLTUft2EPt7gLlRuCN5yfZ0x0akhF1qPVItrviE5BksewBee9u/CfCRln2W99thwLs7iGE9Um2tXBzvKcYQ6ZEcL9D2QXwvF96/Tt63HWzbJqRa26xCjL+i7QM/W5dZ2M8D6VibYyDpBElP5WbL+aRa9nrlZ11Om+3O7/uREu5yMdJ2u60G+UKnraziM3I2puQZOZI2ITW/7AFMjoi3JU0hPxQuIl4mNR8haQxwq6S7I2JaN+M4gfQr+iMR8bKk7YBHWtbTjlnAUElrF75QN+pg+tdItYUWxQTyAuWfw/Uf0i/7TUjNmZD204vQ/vbn5d0VER/tIJ5y63qD9HDDqSXjXgI+0DIgSaRtfbELy30hL7Nc+ZvAehGxpBtxtmjvbt2t5fn61Umk8+eJiFgqaR7Ljmtnd/x+ibTvW7TUrl8hPdPJ6oxrWrayjpG0oaShpIviV5eMfwfpi+VVAElfJNW0yMOf0rIOE/PytG+vQBzrkGpe83Msp3U2Q0TMAP4JTJS0hqSRwAEdzDKF9Kyl/pJ2BD5ZGHclsKekT0vqJ2mYpO0i4m3gGuAMSevkJH48qRmyo+2/EdhC0ufz+vpL+nCx80SZ7VkKXAr8WNJ7JPWVNFLSmjmG/STtofTwvhNICee+zvYT6blJX8zz9pG0gaT3RXoe0i3AuZIG5XGbS9q1C8uElDg262SadUhJ5lWgn6T/Jl3jLC5jhHLHljL+APw/pQ43A4EzgatXMMlaDXDSspX1e9IX17P59f3iyIh4knTtaTLpC+YDpA4bLT4MPCBpEanTxbER8dwKxHEe6WL6f4D7gb92cb7DgJGkZ2x9n5R032xn2u+SahzzgP8hbTsAEfE86ZreCcBcUoLbNo/+OqmW9ixwb57v0jyu7PZHxELSAzUPJdUWXgbOJnWq6Mg3Sc8r+keO42ygT0Q8TerQ8DPSPjqA9PylxZ0sj4h4kNQh5CekDhl3saz28gVS8+eTeb/8ERheZjHl/BT4ZO5ZeH4709wM3ETqvDKDVJMsNh9em//OkfRwmfkvJT07627guTz/17sYn9UgP0/LVpik6aQL3rdWO5aeotTl/F8R0WlNzcwqzzUtW63lJrfNc9PW3sB4UjduM6tB7ohhq7zc9FbOPsAQ0v/uDCM93vzoiHikUrGtCElP0LZzQYuvRMSVlY7HrJLcPGhmZnXDzYNmZlY3nLTMzKxuOGmZmVndcNIyM7O64aRlZmZ1w0nLzMzqxv8H40+KQgntPjIAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "col = 'plasma_glucose_concentration'\n",
    "plt.hist(pima[pima['onset_diabetes']==0][col], 10, alpha=0.5, label='non-diabetes')\n",
    "plt.hist(pima[pima['onset_diabetes']==1][col], 10, alpha=0.5, label='diabetes')\n",
    "plt.legend(loc='upper right')\n",
    "plt.xlabel(col)\n",
    "plt.ylabel('Frequency')\n",
    "plt.title('Histogram of {}'.format(col))\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "for col in ['bmi', 'diastolic_blood_pressure', 'plasma_glucose_concentration']:\n",
    "    plt.hist(pima[pima['onset_diabetes']==0][col], 10, alpha=0.5, label='non-diabetes')\n",
    "    plt.hist(pima[pima['onset_diabetes']==1][col], 10, alpha=0.5, label='diabetes')\n",
    "    plt.legend(loc='upper right')\n",
    "    plt.xlabel(col)\n",
    "    plt.ylabel('Frequency')\n",
    "    plt.title('Histogram of {}'.format(col))\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "times_pregnant                  0.221898\n",
       "plasma_glucose_concentration    0.466581\n",
       "diastolic_blood_pressure        0.065068\n",
       "triceps_thickness               0.074752\n",
       "serum_insulin                   0.130548\n",
       "bmi                             0.292695\n",
       "pedigree_function               0.173844\n",
       "age                             0.238356\n",
       "onset_diabetes                  1.000000\n",
       "Name: onset_diabetes, dtype: float64"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#计算相关性\n",
    "pima.corr()['onset_diabetes']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1a1b378ac8>"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.heatmap(pima.corr())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(768, 9)"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pima.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "times_pregnant                  0\n",
       "plasma_glucose_concentration    0\n",
       "diastolic_blood_pressure        0\n",
       "triceps_thickness               0\n",
       "serum_insulin                   0\n",
       "bmi                             0\n",
       "pedigree_function               0\n",
       "age                             0\n",
       "onset_diabetes                  0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pima.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>times_pregnant</th>\n",
       "      <th>plasma_glucose_concentration</th>\n",
       "      <th>diastolic_blood_pressure</th>\n",
       "      <th>triceps_thickness</th>\n",
       "      <th>serum_insulin</th>\n",
       "      <th>bmi</th>\n",
       "      <th>pedigree_function</th>\n",
       "      <th>age</th>\n",
       "      <th>onset_diabetes</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>768.000000</td>\n",
       "      <td>768.000000</td>\n",
       "      <td>768.000000</td>\n",
       "      <td>768.000000</td>\n",
       "      <td>768.000000</td>\n",
       "      <td>768.000000</td>\n",
       "      <td>768.000000</td>\n",
       "      <td>768.000000</td>\n",
       "      <td>768.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>3.845052</td>\n",
       "      <td>120.894531</td>\n",
       "      <td>69.105469</td>\n",
       "      <td>20.536458</td>\n",
       "      <td>79.799479</td>\n",
       "      <td>31.992578</td>\n",
       "      <td>0.471876</td>\n",
       "      <td>33.240885</td>\n",
       "      <td>0.348958</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>3.369578</td>\n",
       "      <td>31.972618</td>\n",
       "      <td>19.355807</td>\n",
       "      <td>15.952218</td>\n",
       "      <td>115.244002</td>\n",
       "      <td>7.884160</td>\n",
       "      <td>0.331329</td>\n",
       "      <td>11.760232</td>\n",
       "      <td>0.476951</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.078000</td>\n",
       "      <td>21.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>99.000000</td>\n",
       "      <td>62.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>27.300000</td>\n",
       "      <td>0.243750</td>\n",
       "      <td>24.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>3.000000</td>\n",
       "      <td>117.000000</td>\n",
       "      <td>72.000000</td>\n",
       "      <td>23.000000</td>\n",
       "      <td>30.500000</td>\n",
       "      <td>32.000000</td>\n",
       "      <td>0.372500</td>\n",
       "      <td>29.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>6.000000</td>\n",
       "      <td>140.250000</td>\n",
       "      <td>80.000000</td>\n",
       "      <td>32.000000</td>\n",
       "      <td>127.250000</td>\n",
       "      <td>36.600000</td>\n",
       "      <td>0.626250</td>\n",
       "      <td>41.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>17.000000</td>\n",
       "      <td>199.000000</td>\n",
       "      <td>122.000000</td>\n",
       "      <td>99.000000</td>\n",
       "      <td>846.000000</td>\n",
       "      <td>67.100000</td>\n",
       "      <td>2.420000</td>\n",
       "      <td>81.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       times_pregnant  plasma_glucose_concentration  diastolic_blood_pressure  \\\n",
       "count      768.000000                    768.000000                768.000000   \n",
       "mean         3.845052                    120.894531                 69.105469   \n",
       "std          3.369578                     31.972618                 19.355807   \n",
       "min          0.000000                      0.000000                  0.000000   \n",
       "25%          1.000000                     99.000000                 62.000000   \n",
       "50%          3.000000                    117.000000                 72.000000   \n",
       "75%          6.000000                    140.250000                 80.000000   \n",
       "max         17.000000                    199.000000                122.000000   \n",
       "\n",
       "       triceps_thickness  serum_insulin         bmi  pedigree_function  \\\n",
       "count         768.000000     768.000000  768.000000         768.000000   \n",
       "mean           20.536458      79.799479   31.992578           0.471876   \n",
       "std            15.952218     115.244002    7.884160           0.331329   \n",
       "min             0.000000       0.000000    0.000000           0.078000   \n",
       "25%             0.000000       0.000000   27.300000           0.243750   \n",
       "50%            23.000000      30.500000   32.000000           0.372500   \n",
       "75%            32.000000     127.250000   36.600000           0.626250   \n",
       "max            99.000000     846.000000   67.100000           2.420000   \n",
       "\n",
       "              age  onset_diabetes  \n",
       "count  768.000000      768.000000  \n",
       "mean    33.240885        0.348958  \n",
       "std     11.760232        0.476951  \n",
       "min     21.000000        0.000000  \n",
       "25%     24.000000        0.000000  \n",
       "50%     29.000000        0.000000  \n",
       "75%     41.000000        1.000000  \n",
       "max     81.000000        1.000000  "
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pima.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 处理缺失值"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "374"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#将数据中填充为0的数据转换为None\n",
    "pima['serum_insulin'] = pima['serum_insulin'].map(lambda x:x if x != 0 else None)\n",
    "pima['serum_insulin'].isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "times_pregnant                    0\n",
       "plasma_glucose_concentration      5\n",
       "diastolic_blood_pressure         35\n",
       "triceps_thickness               227\n",
       "serum_insulin                   374\n",
       "bmi                              11\n",
       "pedigree_function                 0\n",
       "age                               0\n",
       "onset_diabetes                    0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#将数据中所有的被填充为0的数据转换为None，这样方便操作\n",
    "for col in ['serum_insulin', 'bmi', 'plasma_glucose_concentration', 'diastolic_blood_pressure', 'triceps_thickness']:\n",
    "    pima[col].replace([0], [None], inplace=True)\n",
    "pima.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "    .dataframe thead th {\n",
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>times_pregnant</th>\n",
       "      <th>plasma_glucose_concentration</th>\n",
       "      <th>diastolic_blood_pressure</th>\n",
       "      <th>triceps_thickness</th>\n",
       "      <th>serum_insulin</th>\n",
       "      <th>bmi</th>\n",
       "      <th>pedigree_function</th>\n",
       "      <th>age</th>\n",
       "      <th>onset_diabetes</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>6</td>\n",
       "      <td>148</td>\n",
       "      <td>72</td>\n",
       "      <td>35</td>\n",
       "      <td>NaN</td>\n",
       "      <td>33.6</td>\n",
       "      <td>0.627</td>\n",
       "      <td>50</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>85</td>\n",
       "      <td>66</td>\n",
       "      <td>29</td>\n",
       "      <td>NaN</td>\n",
       "      <td>26.6</td>\n",
       "      <td>0.351</td>\n",
       "      <td>31</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>8</td>\n",
       "      <td>183</td>\n",
       "      <td>64</td>\n",
       "      <td>None</td>\n",
       "      <td>NaN</td>\n",
       "      <td>23.3</td>\n",
       "      <td>0.672</td>\n",
       "      <td>32</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>89</td>\n",
       "      <td>66</td>\n",
       "      <td>23</td>\n",
       "      <td>94.0</td>\n",
       "      <td>28.1</td>\n",
       "      <td>0.167</td>\n",
       "      <td>21</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0</td>\n",
       "      <td>137</td>\n",
       "      <td>40</td>\n",
       "      <td>35</td>\n",
       "      <td>168.0</td>\n",
       "      <td>43.1</td>\n",
       "      <td>2.288</td>\n",
       "      <td>33</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   times_pregnant plasma_glucose_concentration diastolic_blood_pressure  \\\n",
       "0               6                          148                       72   \n",
       "1               1                           85                       66   \n",
       "2               8                          183                       64   \n",
       "3               1                           89                       66   \n",
       "4               0                          137                       40   \n",
       "\n",
       "  triceps_thickness  serum_insulin   bmi  pedigree_function  age  \\\n",
       "0                35            NaN  33.6              0.627   50   \n",
       "1                29            NaN  26.6              0.351   31   \n",
       "2              None            NaN  23.3              0.672   32   \n",
       "3                23           94.0  28.1              0.167   21   \n",
       "4                35          168.0  43.1              2.288   33   \n",
       "\n",
       "   onset_diabetes  \n",
       "0               1  \n",
       "1               0  \n",
       "2               1  \n",
       "3               0  \n",
       "4               1  "
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pima.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>times_pregnant</th>\n",
       "      <th>serum_insulin</th>\n",
       "      <th>pedigree_function</th>\n",
       "      <th>age</th>\n",
       "      <th>onset_diabetes</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>768.000000</td>\n",
       "      <td>394.000000</td>\n",
       "      <td>768.000000</td>\n",
       "      <td>768.000000</td>\n",
       "      <td>768.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>3.845052</td>\n",
       "      <td>155.548223</td>\n",
       "      <td>0.471876</td>\n",
       "      <td>33.240885</td>\n",
       "      <td>0.348958</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>3.369578</td>\n",
       "      <td>118.775855</td>\n",
       "      <td>0.331329</td>\n",
       "      <td>11.760232</td>\n",
       "      <td>0.476951</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>14.000000</td>\n",
       "      <td>0.078000</td>\n",
       "      <td>21.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>76.250000</td>\n",
       "      <td>0.243750</td>\n",
       "      <td>24.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>3.000000</td>\n",
       "      <td>125.000000</td>\n",
       "      <td>0.372500</td>\n",
       "      <td>29.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>6.000000</td>\n",
       "      <td>190.000000</td>\n",
       "      <td>0.626250</td>\n",
       "      <td>41.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>17.000000</td>\n",
       "      <td>846.000000</td>\n",
       "      <td>2.420000</td>\n",
       "      <td>81.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       times_pregnant  serum_insulin  pedigree_function         age  \\\n",
       "count      768.000000     394.000000         768.000000  768.000000   \n",
       "mean         3.845052     155.548223           0.471876   33.240885   \n",
       "std          3.369578     118.775855           0.331329   11.760232   \n",
       "min          0.000000      14.000000           0.078000   21.000000   \n",
       "25%          1.000000      76.250000           0.243750   24.000000   \n",
       "50%          3.000000     125.000000           0.372500   29.000000   \n",
       "75%          6.000000     190.000000           0.626250   41.000000   \n",
       "max         17.000000     846.000000           2.420000   81.000000   \n",
       "\n",
       "       onset_diabetes  \n",
       "count      768.000000  \n",
       "mean         0.348958  \n",
       "std          0.476951  \n",
       "min          0.000000  \n",
       "25%          0.000000  \n",
       "50%          0.000000  \n",
       "75%          1.000000  \n",
       "max          1.000000  "
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pima.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(121.6867627785059, 30.53564107280403)"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pima['plasma_glucose_concentration'].mean(), pima['plasma_glucose_concentration'].std()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "75     None\n",
       "182    None\n",
       "342    None\n",
       "349    None\n",
       "502    None\n",
       "Name: plasma_glucose_concentration, dtype: object"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "empty_plasma_index = pima[pima['plasma_glucose_concentration'].isnull()].index\n",
    "pima.loc[empty_plasma_index]['plasma_glucose_concentration']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 2.1 填充缺失值"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "pima['plasma_glucose_concentration'].fillna(pima['plasma_glucose_concentration'].mean(), inplace=True)\n",
    "#用均值填充缺失值"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "times_pregnant                    0\n",
       "plasma_glucose_concentration      0\n",
       "diastolic_blood_pressure         35\n",
       "triceps_thickness               227\n",
       "serum_insulin                   374\n",
       "bmi                              11\n",
       "pedigree_function                 0\n",
       "age                               0\n",
       "onset_diabetes                    0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pima.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "75     121.686763\n",
       "182    121.686763\n",
       "342    121.686763\n",
       "349    121.686763\n",
       "502    121.686763\n",
       "Name: plasma_glucose_concentration, dtype: float64"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pima.loc[empty_plasma_index]['plasma_glucose_concentration']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/super/anaconda3/lib/python3.7/site-packages/sklearn/utils/deprecation.py:66: DeprecationWarning: Class Imputer is deprecated; Imputer was deprecated in version 0.20 and will be removed in 0.22. Import impute.SimpleImputer from sklearn instead.\n",
      "  warnings.warn(msg, category=DeprecationWarning)\n"
     ]
    },
    {
     "data": {
      "text/html": [
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       "    .dataframe tbody tr th {\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>times_pregnant</th>\n",
       "      <th>plasma_glucose_concentration</th>\n",
       "      <th>diastolic_blood_pressure</th>\n",
       "      <th>triceps_thickness</th>\n",
       "      <th>serum_insulin</th>\n",
       "      <th>bmi</th>\n",
       "      <th>pedigree_function</th>\n",
       "      <th>age</th>\n",
       "      <th>onset_diabetes</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>6.0</td>\n",
       "      <td>148.0</td>\n",
       "      <td>72.0</td>\n",
       "      <td>35.00000</td>\n",
       "      <td>155.548223</td>\n",
       "      <td>33.6</td>\n",
       "      <td>0.627</td>\n",
       "      <td>50.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1.0</td>\n",
       "      <td>85.0</td>\n",
       "      <td>66.0</td>\n",
       "      <td>29.00000</td>\n",
       "      <td>155.548223</td>\n",
       "      <td>26.6</td>\n",
       "      <td>0.351</td>\n",
       "      <td>31.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>8.0</td>\n",
       "      <td>183.0</td>\n",
       "      <td>64.0</td>\n",
       "      <td>29.15342</td>\n",
       "      <td>155.548223</td>\n",
       "      <td>23.3</td>\n",
       "      <td>0.672</td>\n",
       "      <td>32.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1.0</td>\n",
       "      <td>89.0</td>\n",
       "      <td>66.0</td>\n",
       "      <td>23.00000</td>\n",
       "      <td>94.000000</td>\n",
       "      <td>28.1</td>\n",
       "      <td>0.167</td>\n",
       "      <td>21.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.0</td>\n",
       "      <td>137.0</td>\n",
       "      <td>40.0</td>\n",
       "      <td>35.00000</td>\n",
       "      <td>168.000000</td>\n",
       "      <td>43.1</td>\n",
       "      <td>2.288</td>\n",
       "      <td>33.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   times_pregnant  plasma_glucose_concentration  diastolic_blood_pressure  \\\n",
       "0             6.0                         148.0                      72.0   \n",
       "1             1.0                          85.0                      66.0   \n",
       "2             8.0                         183.0                      64.0   \n",
       "3             1.0                          89.0                      66.0   \n",
       "4             0.0                         137.0                      40.0   \n",
       "\n",
       "   triceps_thickness  serum_insulin   bmi  pedigree_function   age  \\\n",
       "0           35.00000     155.548223  33.6              0.627  50.0   \n",
       "1           29.00000     155.548223  26.6              0.351  31.0   \n",
       "2           29.15342     155.548223  23.3              0.672  32.0   \n",
       "3           23.00000      94.000000  28.1              0.167  21.0   \n",
       "4           35.00000     168.000000  43.1              2.288  33.0   \n",
       "\n",
       "   onset_diabetes  \n",
       "0             1.0  \n",
       "1             0.0  \n",
       "2             1.0  \n",
       "3             0.0  \n",
       "4             1.0  "
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#使用Imputer填充数据\n",
    "from sklearn.preprocessing import Imputer\n",
    "imputer = Imputer(strategy='mean')\n",
    "pima_imputed = imputer.fit_transform(pima)\n",
    "type(pima_imputed)\n",
    "pima_imputed = pd.DataFrame(pima_imputed, columns=pima_column_names)\n",
    "pima_imputed.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "times_pregnant                    0\n",
       "plasma_glucose_concentration      0\n",
       "diastolic_blood_pressure         35\n",
       "triceps_thickness               227\n",
       "serum_insulin                   374\n",
       "bmi                              11\n",
       "pedigree_function                 0\n",
       "age                               0\n",
       "onset_diabetes                    0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pima.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "75     121.686763\n",
       "182    121.686763\n",
       "342    121.686763\n",
       "349    121.686763\n",
       "502    121.686763\n",
       "Name: plasma_glucose_concentration, dtype: float64"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pima_imputed.loc[empty_plasma_index]['plasma_glucose_concentration']  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "times_pregnant                  0\n",
       "plasma_glucose_concentration    0\n",
       "diastolic_blood_pressure        0\n",
       "triceps_thickness               0\n",
       "serum_insulin                   0\n",
       "bmi                             0\n",
       "pedigree_function               0\n",
       "age                             0\n",
       "onset_diabetes                  0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pima_imputed.isnull().sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 2.2 删除缺失值"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [],
   "source": [
    "#删除缺失值\n",
    "pima_dropped = pima.dropna()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "retained 49% of rows\n"
     ]
    }
   ],
   "source": [
    "print(\"retained {}% of rows\".format(round(100*(pima.shape[0] - pima_dropped.shape[0])/float(pima.shape[0]))))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    0.651042\n",
       "1    0.348958\n",
       "Name: onset_diabetes, dtype: float64"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pima['onset_diabetes'].value_counts(normalize=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    0.669211\n",
       "1    0.330789\n",
       "Name: onset_diabetes, dtype: float64"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pima_dropped['onset_diabetes'].value_counts(normalize=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "times_pregnant                    3.845052\n",
       "plasma_glucose_concentration    121.686763\n",
       "diastolic_blood_pressure         72.405184\n",
       "triceps_thickness                29.153420\n",
       "serum_insulin                   155.548223\n",
       "bmi                              32.457464\n",
       "pedigree_function                 0.471876\n",
       "age                              33.240885\n",
       "onset_diabetes                    0.348958\n",
       "dtype: float64"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pima.mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "times_pregnant                    3.295165\n",
       "plasma_glucose_concentration    122.625157\n",
       "diastolic_blood_pressure         70.671756\n",
       "triceps_thickness                29.122137\n",
       "serum_insulin                   155.717557\n",
       "bmi                              33.072519\n",
       "pedigree_function                 0.522476\n",
       "age                              30.839695\n",
       "onset_diabetes                    0.330789\n",
       "dtype: float64"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pima_dropped.mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "times_pregnant                 -0.143012\n",
       "plasma_glucose_concentration    0.007712\n",
       "diastolic_blood_pressure       -0.023941\n",
       "triceps_thickness              -0.001073\n",
       "serum_insulin                   0.001089\n",
       "bmi                             0.018950\n",
       "pedigree_function               0.107230\n",
       "age                            -0.072236\n",
       "onset_diabetes                 -0.052068\n",
       "dtype: float64"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "(pima_dropped.mean() - pima.mean()) / pima.mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, '% change')"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "ax = ((pima_dropped.mean() - pima.mean()) / pima.mean()).plot(kind='bar', title='% change in average column values')\n",
    "ax.set_ylabel('% change')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "learning from 393 rows\n",
      "0.7455470737913485 {'n_neighbors': 7}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/super/anaconda3/lib/python3.7/site-packages/sklearn/model_selection/_split.py:1978: FutureWarning: The default value of cv will change from 3 to 5 in version 0.22. Specify it explicitly to silence this warning.\n",
      "  warnings.warn(CV_WARNING, FutureWarning)\n",
      "/Users/super/anaconda3/lib/python3.7/site-packages/sklearn/model_selection/_search.py:813: DeprecationWarning: The default of the `iid` parameter will change from True to False in version 0.22 and will be removed in 0.24. This will change numeric results when test-set sizes are unequal.\n",
      "  DeprecationWarning)\n"
     ]
    }
   ],
   "source": [
    "from sklearn.neighbors import KNeighborsClassifier\n",
    "from sklearn.model_selection import GridSearchCV\n",
    "\n",
    "X_dropped = pima_dropped.drop('onset_diabetes', axis=1)\n",
    "print(\"learning from {} rows\".format(X_dropped.shape[0]))\n",
    "y_dropped = pima_dropped['onset_diabetes']\n",
    "\n",
    "# KNN parameters to try\n",
    "knn_params = {'n_neighbors':[1, 2, 3, 4, 5, 6, 7]}\n",
    "\n",
    "knn = KNeighborsClassifier()\n",
    "grid = GridSearchCV(knn, knn_params)\n",
    "grid.fit(X_dropped, y_dropped)\n",
    "\n",
    "print(grid.best_score_, grid.best_params_)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "learning from 768 rows\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/super/anaconda3/lib/python3.7/site-packages/sklearn/model_selection/_split.py:1978: FutureWarning: The default value of cv will change from 3 to 5 in version 0.22. Specify it explicitly to silence this warning.\n",
      "  warnings.warn(CV_WARNING, FutureWarning)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.7330729166666666 {'n_neighbors': 6}\n"
     ]
    }
   ],
   "source": [
    "pima_zero = pima.fillna(0)  # impute values with 0\n",
    "\n",
    "X_zero = pima_zero.drop('onset_diabetes', axis=1)\n",
    "print(\"learning from {} rows\".format(X_zero.shape[0]))\n",
    "y_zero = pima_zero['onset_diabetes']\n",
    "\n",
    "knn_params = {'n_neighbors':[1, 2, 3, 4, 5, 6, 7]}\n",
    "grid = GridSearchCV(knn, knn_params)\n",
    "grid.fit(X_zero, y_zero)\n",
    "\n",
    "print(grid.best_score_, grid.best_params_)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 2.3 在机器学习Pipline中填充值"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "serum_insulin    374\n",
       "dtype: int64"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "\n",
    "X = pima[['serum_insulin']].copy()\n",
    "y = pima['onset_diabetes'].copy()\n",
    "\n",
    "X.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "serum_insulin    374\n",
       "dtype: int64"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#先处理训练集再处理测试集，而不是直接对整个数据集进行操作\n",
    "from sklearn.model_selection import train_test_split\n",
    "\n",
    "X = pima[['serum_insulin']].copy()\n",
    "y = pima['onset_diabetes'].copy()\n",
    "\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, random_state=99)\n",
    "\n",
    "X.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [],
   "source": [
    "training_mean = X_train.mean()\n",
    "X_train = X_train.fillna(training_mean)\n",
    "X_test = X_test.fillna(training_mean)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.4895833333333333"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "knn = KNeighborsClassifier()\n",
    "\n",
    "knn.fit(X_train, y_train)\n",
    "\n",
    "knn.score(X_test, y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/super/anaconda3/lib/python3.7/site-packages/sklearn/utils/deprecation.py:66: DeprecationWarning: Class Imputer is deprecated; Imputer was deprecated in version 0.20 and will be removed in 0.22. Import impute.SimpleImputer from sklearn instead.\n",
      "  warnings.warn(msg, category=DeprecationWarning)\n",
      "/Users/super/anaconda3/lib/python3.7/site-packages/sklearn/model_selection/_split.py:1978: FutureWarning: The default value of cv will change from 3 to 5 in version 0.22. Specify it explicitly to silence this warning.\n",
      "  warnings.warn(CV_WARNING, FutureWarning)\n",
      "/Users/super/anaconda3/lib/python3.7/site-packages/sklearn/utils/deprecation.py:66: DeprecationWarning: Class Imputer is deprecated; Imputer was deprecated in version 0.20 and will be removed in 0.22. Import impute.SimpleImputer from sklearn instead.\n",
      "  warnings.warn(msg, category=DeprecationWarning)\n",
      "/Users/super/anaconda3/lib/python3.7/site-packages/sklearn/utils/deprecation.py:66: DeprecationWarning: Class Imputer is deprecated; Imputer was deprecated in version 0.20 and will be removed in 0.22. Import impute.SimpleImputer from sklearn instead.\n",
      "  warnings.warn(msg, category=DeprecationWarning)\n",
      "/Users/super/anaconda3/lib/python3.7/site-packages/sklearn/utils/deprecation.py:66: DeprecationWarning: Class Imputer is deprecated; Imputer was deprecated in version 0.20 and will be removed in 0.22. Import impute.SimpleImputer from sklearn instead.\n",
      "  warnings.warn(msg, category=DeprecationWarning)\n",
      "/Users/super/anaconda3/lib/python3.7/site-packages/sklearn/utils/deprecation.py:66: DeprecationWarning: Class Imputer is deprecated; Imputer was deprecated in version 0.20 and will be removed in 0.22. Import impute.SimpleImputer from sklearn instead.\n",
      "  warnings.warn(msg, category=DeprecationWarning)\n",
      "/Users/super/anaconda3/lib/python3.7/site-packages/sklearn/utils/deprecation.py:66: DeprecationWarning: Class Imputer is deprecated; Imputer was deprecated in version 0.20 and will be removed in 0.22. Import impute.SimpleImputer from sklearn instead.\n",
      "  warnings.warn(msg, category=DeprecationWarning)\n",
      "/Users/super/anaconda3/lib/python3.7/site-packages/sklearn/utils/deprecation.py:66: DeprecationWarning: Class Imputer is deprecated; Imputer was deprecated in version 0.20 and will be removed in 0.22. Import impute.SimpleImputer from sklearn instead.\n",
      "  warnings.warn(msg, category=DeprecationWarning)\n",
      "/Users/super/anaconda3/lib/python3.7/site-packages/sklearn/utils/deprecation.py:66: DeprecationWarning: Class Imputer is deprecated; Imputer was deprecated in version 0.20 and will be removed in 0.22. Import impute.SimpleImputer from sklearn instead.\n",
      "  warnings.warn(msg, category=DeprecationWarning)\n",
      "/Users/super/anaconda3/lib/python3.7/site-packages/sklearn/utils/deprecation.py:66: DeprecationWarning: Class Imputer is deprecated; Imputer was deprecated in version 0.20 and will be removed in 0.22. Import impute.SimpleImputer from sklearn instead.\n",
      "  warnings.warn(msg, category=DeprecationWarning)\n",
      "/Users/super/anaconda3/lib/python3.7/site-packages/sklearn/utils/deprecation.py:66: DeprecationWarning: Class Imputer is deprecated; Imputer was deprecated in version 0.20 and will be removed in 0.22. Import impute.SimpleImputer from sklearn instead.\n",
      "  warnings.warn(msg, category=DeprecationWarning)\n",
      "/Users/super/anaconda3/lib/python3.7/site-packages/sklearn/utils/deprecation.py:66: DeprecationWarning: Class Imputer is deprecated; Imputer was deprecated in version 0.20 and will be removed in 0.22. Import impute.SimpleImputer from sklearn instead.\n",
      "  warnings.warn(msg, category=DeprecationWarning)\n",
      "/Users/super/anaconda3/lib/python3.7/site-packages/sklearn/utils/deprecation.py:66: DeprecationWarning: Class Imputer is deprecated; Imputer was deprecated in version 0.20 and will be removed in 0.22. Import impute.SimpleImputer from sklearn instead.\n",
      "  warnings.warn(msg, category=DeprecationWarning)\n",
      "/Users/super/anaconda3/lib/python3.7/site-packages/sklearn/utils/deprecation.py:66: DeprecationWarning: Class Imputer is deprecated; Imputer was deprecated in version 0.20 and will be removed in 0.22. Import impute.SimpleImputer from sklearn instead.\n",
      "  warnings.warn(msg, category=DeprecationWarning)\n",
      "/Users/super/anaconda3/lib/python3.7/site-packages/sklearn/utils/deprecation.py:66: DeprecationWarning: Class Imputer is deprecated; Imputer was deprecated in version 0.20 and will be removed in 0.22. Import impute.SimpleImputer from sklearn instead.\n",
      "  warnings.warn(msg, category=DeprecationWarning)\n",
      "/Users/super/anaconda3/lib/python3.7/site-packages/sklearn/utils/deprecation.py:66: DeprecationWarning: Class Imputer is deprecated; Imputer was deprecated in version 0.20 and will be removed in 0.22. Import impute.SimpleImputer from sklearn instead.\n",
      "  warnings.warn(msg, category=DeprecationWarning)\n",
      "/Users/super/anaconda3/lib/python3.7/site-packages/sklearn/utils/deprecation.py:66: DeprecationWarning: Class Imputer is deprecated; Imputer was deprecated in version 0.20 and will be removed in 0.22. Import impute.SimpleImputer from sklearn instead.\n",
      "  warnings.warn(msg, category=DeprecationWarning)\n",
      "/Users/super/anaconda3/lib/python3.7/site-packages/sklearn/utils/deprecation.py:66: DeprecationWarning: Class Imputer is deprecated; Imputer was deprecated in version 0.20 and will be removed in 0.22. Import impute.SimpleImputer from sklearn instead.\n",
      "  warnings.warn(msg, category=DeprecationWarning)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.7317708333333334 {'classify__n_neighbors': 6}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/super/anaconda3/lib/python3.7/site-packages/sklearn/utils/deprecation.py:66: DeprecationWarning: Class Imputer is deprecated; Imputer was deprecated in version 0.20 and will be removed in 0.22. Import impute.SimpleImputer from sklearn instead.\n",
      "  warnings.warn(msg, category=DeprecationWarning)\n",
      "/Users/super/anaconda3/lib/python3.7/site-packages/sklearn/utils/deprecation.py:66: DeprecationWarning: Class Imputer is deprecated; Imputer was deprecated in version 0.20 and will be removed in 0.22. Import impute.SimpleImputer from sklearn instead.\n",
      "  warnings.warn(msg, category=DeprecationWarning)\n",
      "/Users/super/anaconda3/lib/python3.7/site-packages/sklearn/utils/deprecation.py:66: DeprecationWarning: Class Imputer is deprecated; Imputer was deprecated in version 0.20 and will be removed in 0.22. Import impute.SimpleImputer from sklearn instead.\n",
      "  warnings.warn(msg, category=DeprecationWarning)\n",
      "/Users/super/anaconda3/lib/python3.7/site-packages/sklearn/utils/deprecation.py:66: DeprecationWarning: Class Imputer is deprecated; Imputer was deprecated in version 0.20 and will be removed in 0.22. Import impute.SimpleImputer from sklearn instead.\n",
      "  warnings.warn(msg, category=DeprecationWarning)\n",
      "/Users/super/anaconda3/lib/python3.7/site-packages/sklearn/utils/deprecation.py:66: DeprecationWarning: Class Imputer is deprecated; Imputer was deprecated in version 0.20 and will be removed in 0.22. Import impute.SimpleImputer from sklearn instead.\n",
      "  warnings.warn(msg, category=DeprecationWarning)\n",
      "/Users/super/anaconda3/lib/python3.7/site-packages/sklearn/utils/deprecation.py:66: DeprecationWarning: Class Imputer is deprecated; Imputer was deprecated in version 0.20 and will be removed in 0.22. Import impute.SimpleImputer from sklearn instead.\n",
      "  warnings.warn(msg, category=DeprecationWarning)\n",
      "/Users/super/anaconda3/lib/python3.7/site-packages/sklearn/utils/deprecation.py:66: DeprecationWarning: Class Imputer is deprecated; Imputer was deprecated in version 0.20 and will be removed in 0.22. Import impute.SimpleImputer from sklearn instead.\n",
      "  warnings.warn(msg, category=DeprecationWarning)\n"
     ]
    }
   ],
   "source": [
    "from sklearn.pipeline import Pipeline\n",
    "knn_params = {'classify__n_neighbors':[1, 2, 3, 4, 5, 6, 7]}\n",
    "# must redefine params to fit the pipeline\n",
    "\n",
    "mean_impute = Pipeline([('imputer', Imputer(strategy='mean')), ('classify', knn)])\n",
    "X = pima.drop('onset_diabetes', axis=1)\n",
    "y = pima['onset_diabetes']\n",
    "\n",
    "grid = GridSearchCV(mean_impute, knn_params)\n",
    "grid.fit(X, y)\n",
    "\n",
    "print(grid.best_score_, grid.best_params_)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/super/anaconda3/lib/python3.7/site-packages/sklearn/utils/deprecation.py:66: DeprecationWarning: Class Imputer is deprecated; Imputer was deprecated in version 0.20 and will be removed in 0.22. Import impute.SimpleImputer from sklearn instead.\n",
      "  warnings.warn(msg, category=DeprecationWarning)\n",
      "/Users/super/anaconda3/lib/python3.7/site-packages/sklearn/model_selection/_split.py:1978: FutureWarning: The default value of cv will change from 3 to 5 in version 0.22. Specify it explicitly to silence this warning.\n",
      "  warnings.warn(CV_WARNING, FutureWarning)\n",
      "/Users/super/anaconda3/lib/python3.7/site-packages/sklearn/utils/deprecation.py:66: DeprecationWarning: Class Imputer is deprecated; Imputer was deprecated in version 0.20 and will be removed in 0.22. Import impute.SimpleImputer from sklearn instead.\n",
      "  warnings.warn(msg, category=DeprecationWarning)\n",
      "/Users/super/anaconda3/lib/python3.7/site-packages/sklearn/utils/deprecation.py:66: DeprecationWarning: Class Imputer is deprecated; Imputer was deprecated in version 0.20 and will be removed in 0.22. Import impute.SimpleImputer from sklearn instead.\n",
      "  warnings.warn(msg, category=DeprecationWarning)\n",
      "/Users/super/anaconda3/lib/python3.7/site-packages/sklearn/utils/deprecation.py:66: DeprecationWarning: Class Imputer is deprecated; Imputer was deprecated in version 0.20 and will be removed in 0.22. Import impute.SimpleImputer from sklearn instead.\n",
      "  warnings.warn(msg, category=DeprecationWarning)\n",
      "/Users/super/anaconda3/lib/python3.7/site-packages/sklearn/utils/deprecation.py:66: DeprecationWarning: Class Imputer is deprecated; Imputer was deprecated in version 0.20 and will be removed in 0.22. Import impute.SimpleImputer from sklearn instead.\n",
      "  warnings.warn(msg, category=DeprecationWarning)\n",
      "/Users/super/anaconda3/lib/python3.7/site-packages/sklearn/utils/deprecation.py:66: DeprecationWarning: Class Imputer is deprecated; Imputer was deprecated in version 0.20 and will be removed in 0.22. Import impute.SimpleImputer from sklearn instead.\n",
      "  warnings.warn(msg, category=DeprecationWarning)\n",
      "/Users/super/anaconda3/lib/python3.7/site-packages/sklearn/utils/deprecation.py:66: DeprecationWarning: Class Imputer is deprecated; Imputer was deprecated in version 0.20 and will be removed in 0.22. Import impute.SimpleImputer from sklearn instead.\n",
      "  warnings.warn(msg, category=DeprecationWarning)\n",
      "/Users/super/anaconda3/lib/python3.7/site-packages/sklearn/utils/deprecation.py:66: DeprecationWarning: Class Imputer is deprecated; Imputer was deprecated in version 0.20 and will be removed in 0.22. Import impute.SimpleImputer from sklearn instead.\n",
      "  warnings.warn(msg, category=DeprecationWarning)\n",
      "/Users/super/anaconda3/lib/python3.7/site-packages/sklearn/utils/deprecation.py:66: DeprecationWarning: Class Imputer is deprecated; Imputer was deprecated in version 0.20 and will be removed in 0.22. Import impute.SimpleImputer from sklearn instead.\n",
      "  warnings.warn(msg, category=DeprecationWarning)\n",
      "/Users/super/anaconda3/lib/python3.7/site-packages/sklearn/utils/deprecation.py:66: DeprecationWarning: Class Imputer is deprecated; Imputer was deprecated in version 0.20 and will be removed in 0.22. Import impute.SimpleImputer from sklearn instead.\n",
      "  warnings.warn(msg, category=DeprecationWarning)\n",
      "/Users/super/anaconda3/lib/python3.7/site-packages/sklearn/utils/deprecation.py:66: DeprecationWarning: Class Imputer is deprecated; Imputer was deprecated in version 0.20 and will be removed in 0.22. Import impute.SimpleImputer from sklearn instead.\n",
      "  warnings.warn(msg, category=DeprecationWarning)\n",
      "/Users/super/anaconda3/lib/python3.7/site-packages/sklearn/utils/deprecation.py:66: DeprecationWarning: Class Imputer is deprecated; Imputer was deprecated in version 0.20 and will be removed in 0.22. Import impute.SimpleImputer from sklearn instead.\n",
      "  warnings.warn(msg, category=DeprecationWarning)\n",
      "/Users/super/anaconda3/lib/python3.7/site-packages/sklearn/utils/deprecation.py:66: DeprecationWarning: Class Imputer is deprecated; Imputer was deprecated in version 0.20 and will be removed in 0.22. Import impute.SimpleImputer from sklearn instead.\n",
      "  warnings.warn(msg, category=DeprecationWarning)\n",
      "/Users/super/anaconda3/lib/python3.7/site-packages/sklearn/utils/deprecation.py:66: DeprecationWarning: Class Imputer is deprecated; Imputer was deprecated in version 0.20 and will be removed in 0.22. Import impute.SimpleImputer from sklearn instead.\n",
      "  warnings.warn(msg, category=DeprecationWarning)\n",
      "/Users/super/anaconda3/lib/python3.7/site-packages/sklearn/utils/deprecation.py:66: DeprecationWarning: Class Imputer is deprecated; Imputer was deprecated in version 0.20 and will be removed in 0.22. Import impute.SimpleImputer from sklearn instead.\n",
      "  warnings.warn(msg, category=DeprecationWarning)\n",
      "/Users/super/anaconda3/lib/python3.7/site-packages/sklearn/utils/deprecation.py:66: DeprecationWarning: Class Imputer is deprecated; Imputer was deprecated in version 0.20 and will be removed in 0.22. Import impute.SimpleImputer from sklearn instead.\n",
      "  warnings.warn(msg, category=DeprecationWarning)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.7369791666666666 {'classify__n_neighbors': 6}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/super/anaconda3/lib/python3.7/site-packages/sklearn/utils/deprecation.py:66: DeprecationWarning: Class Imputer is deprecated; Imputer was deprecated in version 0.20 and will be removed in 0.22. Import impute.SimpleImputer from sklearn instead.\n",
      "  warnings.warn(msg, category=DeprecationWarning)\n",
      "/Users/super/anaconda3/lib/python3.7/site-packages/sklearn/utils/deprecation.py:66: DeprecationWarning: Class Imputer is deprecated; Imputer was deprecated in version 0.20 and will be removed in 0.22. Import impute.SimpleImputer from sklearn instead.\n",
      "  warnings.warn(msg, category=DeprecationWarning)\n",
      "/Users/super/anaconda3/lib/python3.7/site-packages/sklearn/utils/deprecation.py:66: DeprecationWarning: Class Imputer is deprecated; Imputer was deprecated in version 0.20 and will be removed in 0.22. Import impute.SimpleImputer from sklearn instead.\n",
      "  warnings.warn(msg, category=DeprecationWarning)\n",
      "/Users/super/anaconda3/lib/python3.7/site-packages/sklearn/utils/deprecation.py:66: DeprecationWarning: Class Imputer is deprecated; Imputer was deprecated in version 0.20 and will be removed in 0.22. Import impute.SimpleImputer from sklearn instead.\n",
      "  warnings.warn(msg, category=DeprecationWarning)\n",
      "/Users/super/anaconda3/lib/python3.7/site-packages/sklearn/utils/deprecation.py:66: DeprecationWarning: Class Imputer is deprecated; Imputer was deprecated in version 0.20 and will be removed in 0.22. Import impute.SimpleImputer from sklearn instead.\n",
      "  warnings.warn(msg, category=DeprecationWarning)\n",
      "/Users/super/anaconda3/lib/python3.7/site-packages/sklearn/utils/deprecation.py:66: DeprecationWarning: Class Imputer is deprecated; Imputer was deprecated in version 0.20 and will be removed in 0.22. Import impute.SimpleImputer from sklearn instead.\n",
      "  warnings.warn(msg, category=DeprecationWarning)\n",
      "/Users/super/anaconda3/lib/python3.7/site-packages/sklearn/utils/deprecation.py:66: DeprecationWarning: Class Imputer is deprecated; Imputer was deprecated in version 0.20 and will be removed in 0.22. Import impute.SimpleImputer from sklearn instead.\n",
      "  warnings.warn(msg, category=DeprecationWarning)\n",
      "/Users/super/anaconda3/lib/python3.7/site-packages/sklearn/utils/deprecation.py:66: DeprecationWarning: Class Imputer is deprecated; Imputer was deprecated in version 0.20 and will be removed in 0.22. Import impute.SimpleImputer from sklearn instead.\n",
      "  warnings.warn(msg, category=DeprecationWarning)\n"
     ]
    }
   ],
   "source": [
    "knn_params = {'classify__n_neighbors':[1, 2, 3, 4, 5, 6, 7]}\n",
    "\n",
    "median_impute = Pipeline([('imputer', Imputer(strategy='median')), ('classify', knn)])\n",
    "X = pima.drop('onset_diabetes', axis=1)\n",
    "y = pima['onset_diabetes']\n",
    "\n",
    "grid = GridSearchCV(median_impute, knn_params)\n",
    "grid.fit(X, y)\n",
    "\n",
    "print(grid.best_score_, grid.best_params_)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 3. 标准化与归一化"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/super/anaconda3/lib/python3.7/site-packages/sklearn/utils/deprecation.py:66: DeprecationWarning: Class Imputer is deprecated; Imputer was deprecated in version 0.20 and will be removed in 0.22. Import impute.SimpleImputer from sklearn instead.\n",
      "  warnings.warn(msg, category=DeprecationWarning)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "array([[<matplotlib.axes._subplots.AxesSubplot object at 0x1a1c31d080>,\n",
       "        <matplotlib.axes._subplots.AxesSubplot object at 0x1a1c34efd0>,\n",
       "        <matplotlib.axes._subplots.AxesSubplot object at 0x1a1c3809b0>],\n",
       "       [<matplotlib.axes._subplots.AxesSubplot object at 0x1a1c3b7390>,\n",
       "        <matplotlib.axes._subplots.AxesSubplot object at 0x1a1c3e2d30>,\n",
       "        <matplotlib.axes._subplots.AxesSubplot object at 0x1a1c417550>],\n",
       "       [<matplotlib.axes._subplots.AxesSubplot object at 0x1a1c444ef0>,\n",
       "        <matplotlib.axes._subplots.AxesSubplot object at 0x1a1c479908>,\n",
       "        <matplotlib.axes._subplots.AxesSubplot object at 0x1a1c479940>]],\n",
       "      dtype=object)"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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js+9BS6MePyyNfdgQ9PXo2RN4T5JDga/TnKW+h6ZxfVXPahM9eiRpxmxAS9IiSHOq+TTgllLKJ3sWTdz67ySeeEvA9yQ5l2bwsPsmunpLkgb26DkFOIGmt84JwCeAdzDkHj3DMOr7MHX8o9EbaWm/B6Nhtvsw3Z+nNqAlaXHsCbwd+HaS62vaB2kazuclORK4ncfuYnAJsD+wCngIOGJxw5Wk7hrUo6eUsrZn+anAxXV2aD16hmHUe09MG/8I9EZa8u/BCFjIfbABLUmLoJSyksFnQQD2HpC/AEcvaFCSNIIm69EzcTlMnT0QuLE+vwj4+ySfpBlEbAy4ZhFDlrSE2ICWJEnSKJmsR88hSVbQdM++DXgXQCnlpiTnATfTjOB9tCNwS5otG9CSJEkaGVP06LlkinVOBE5csKAkbTBsQEuSBGxx+sJeV3fvEQ76K0nSqNto2AFIkiRJkjQKbEBLkiRJktSCDWhJkiRJklqwAS1JkiRJUgs2oCVJkiRJasEGtCRJkiRJLdiAliRJkiSpBRvQkiRJkiS1YANakiRJkqQWbEBLkiRJktSCDWhJkiRJklqwAS1JkiRJUgs2oCVJkiRJasEGtCRJkiRJLdiAlqRFkuQzSdYlubEn7XNJrq/TbUmur+k7JPlJz7K/GV7kkiRJAthk2AFI0gbkDOAvgbMmEkopvzXxPMkngPt68n+3lLJi0aKTJEnSlGxAS9IiKaVcmWSHQcuSBHgr8IbFjEmSJEntTduFO8n2SS5PckuSm5K8t6Yfn+TOnu6F+/esc1ySVUluTbLPQu6AJC0RrwXWllLGe9J2TPLNJF9N8tphBSZJkqRGmzPQ64FjSinfSPI04Lokl9ZlJ5dSPt6bOcnOwMHALsBzgK8k2amU8sh8Bi5JS8whwDk982uA55ZSfpRkN+ALSXYppdw/aOXx8fFBybM239ubH8uGHcCczPSYdvM9aG/U44fR24exsbFhhyBJS960DehSyhqaihyllAeS3AJsO8UqBwDnllIeBr6fZBWwO/C1eYhXkpacJJsAvw7sNpFWy9CH6/PrknwX2An4+qBtzGfFeXx8vJsV8ZV3DjuCOZnJMe3se9DSqMcPS2MfJEnzb0ajcNdr914OXF2T3pPkhjqy7JY1bVvgjp7VVjN1g1uSNnRvBL5TSlk9kZDkWUk2rs+fB4wB3xtSfJIkSWIGg4gl2Rw4H3hfKeX+JKcAJwClPn4CeAeQAauXybY7Vfeoxy9buK57s+mi1fVuXV2Or8uxQbfj63JsMPv4NpSzPEnOAfYCnplkNfCRUsppNJe9nNOX/XXAHyZZDzwCvLuUcvdixitJkqTHa9WATvIkmsbz2aWUzwOUUtb2LD8VuLjOrga271l9O+CuybY9WcX5CV2nFrDr3kwr713v1tXl+LocG3Q7vi7HBt2PrwtKKYdMkn74gLTzacpdSZIkdUSbUbgDnAbcUkr5ZE/68p5sBwI31ucXAQcn2SzJjjTdDq+Zv5AlSZIkSVp8bc5A7wm8Hfh2kutr2geBQ5KsoOmefRvwLoBSyk1JzgNuphnB+2hH4JYkSZLUdVucPh+9XpdN2nv23iMcGmrUtRmFeyWDr2u+ZIp1TgROnENckiRJkiR1yoxG4ZYkSZIkaUNlA1qSJEmSpBZsQEuSJEmS1IINaEmSJEmSWrABLUmSJElSCzagJUmSNDKSbJ/k8iS3JLkpyXtr+lZJLk0yXh+3rOlJ8qkkq5LckGTX4e6BpFFmA1qSJEmjZD1wTCnlRcAewNFJdgaOBS4rpYwBl9V5gP2AsTodBZyy+CFLWiqmvQ+0JEltbXH6ncMOQdISV0pZA6ypzx9IcguwLXAAsFfNdiZwBfCBmn5WKaUAVyXZIsnyuh1JmhHPQEuSJGkkJdkBeDlwNbDNRKO4Pm5ds20L3NGz2uqaJkkz5hloSZIkjZwkmwPnA+8rpdyfZNKsA9LKZJnHx8fnIbrhGvV9mDr+ZYsWx0IYlfdmVOKcymz3YWxsbMrlNqAlSZI0UpI8iabxfHYp5fM1ee1E1+wky4F1NX01sH3P6tsBd0227ekqz103Pj4+0vswbfwrR/tSoVF4b0b9MwQLuw924ZYkSdLISHOq+TTgllLKJ3sWXQQcVp8fBlzYk35oHY17D+A+r3+WNFuegZYkSdIo2RN4O/DtJNfXtA8CJwHnJTkSuB04qC67BNgfWAU8BByxuOFKWkpsQEuSJGlklFJWMvi6ZoC9B+QvwNELGpSkDYZduCVpkST5TJJ1SW7sSTs+yZ1Jrq/T/j3LjkuyKsmtSfYZTtSSJEmaYANakhbPGcC+A9JPLqWsqNMlAEl2Bg4Gdqnr/HWSjRctUkmSJD2BDWhJWiSllCuBu1tmPwA4t5TycCnl+zTX7u2+YMFJkiRpWjagJWn43pPkhtrFe8uati1wR0+e1TVNkiRJQ+IgYpI0XKcAJwClPn4CeAeDB8gpk21kfHx8XoOa/faWzWscS8lMj+l8v6eLbdTjh9Hbh1G/b6skjQIb0JI0RKWUtRPPk5wKXFxnVwPb92TdDrhrsu3MZ8V5fHx89ttbeee8xbHUzOSYzuk96IBRjx+Wxj5IkuafXbglaYiSLO+ZPRCYGKH7IuDgJJsl2REYA65Z7PgkSZL0GM9AS9IiSXIOsBfwzCSrgY8AeyVZQdM9+zbgXQCllJuSnAfcDKwHji6lPDKMuCVJktSwAS1Ji6SUcsiA5NOmyH8icOLCRSRJkqSZsAu3JEmSJEkt2ICWJEmSJKkFG9CSJEmSJLVgA1qSJEmSpBambUAn2T7J5UluSXJTkvfW9K2SXJpkvD5uWdOT5FNJViW5IcmuC70TkiRJkiQttDZnoNcDx5RSXgTsARydZGfgWOCyUsoYcFmdB9iP5n6lY8BRwCnzHrUkSZIkSYts2gZ0KWVNKeUb9fkDwC3AtsABwJk125nAW+rzA4CzSuMqYIsky+c9ckmSJEmSFtGMroFOsgPwcuBqYJtSyhpoGtnA1jXbtsAdPautrmmSJEmSJI2sTdpmTLI5cD7wvlLK/UkmzTogrUyWeXx8fNLXfPyyZS2inJ2pYpjPdRZTl+PrcmzQ7fi6HBvMPr6xsbF5jkSSJEmaf60a0EmeRNN4PruU8vmavDbJ8lLKmtpFe11NXw1s37P6dsBdk217sorz+Pj445etvLNNqLMy08r7E2LrmC7H1+XYoNvxdTk26H58kiRJ0ly1GYU7wGnALaWUT/Ysugg4rD4/DLiwJ/3QOhr3HsB9E129JUmSJEkaVW3OQO8JvB34dpLra9oHgZOA85IcCdwOHFSXXQLsD6wCHgKOmNeIJUmSJEkagmkb0KWUlQy+rhlg7wH5C3D0HOOSJEmSJKlTZjQKtyRJkiRJGyob0JIkSZIktWADWpIkSZKkFmxAS5IkSZLUgg1oSVokST6TZF2SG3vS/jTJd5LckOSCJFvU9B2S/CTJ9XX6m+FFLkmSJLABLUmL6Qxg3760S4EXl1JeCvwbcFzPsu+WUlbU6d2LFKMkSZImYQNakhZJKeVK4O6+tC+XUtbX2auA7RY9MEmSJLViA1qSuuMdwD/2zO+Y5JtJvprktcMKSpIkSY1Nhh2AJAmSfAhYD5xdk9YAzy2l/CjJbsAXkuxSSrl/0Prj4+PzGs/st7dsXuNYSmZ6TOf7PV1sox4/jN4+jI2NDTsESVrybEBL0pAlOQz4NWDvUkoBKKU8DDxcn1+X5LvATsDXB21jPivO4+Pjs9/eyjvnLY6lZibHdE7vQQeMevywNPZBkjT/7MItSUOUZF/gA8CbSykP9aQ/K8nG9fnzgDHge8OJUpIkSWADWpIWTZJzgK8BL0yyOsmRwF8CTwMu7btd1euAG5J8C/gH4N2llLsHbliSNiCT3BLw+CR39tz6b/+eZcclWZXk1iT7DCdqSUuFXbglaZGUUg4ZkHzaJHnPB85f2IgkaSSdQfPn41l96SeXUj7em5BkZ+BgYBfgOcBXkuxUSnlkMQKVtPR4BlqSJEkjY9AtAadwAHBuKeXhUsr3gVXA7gsWnKQlzzPQkiRJWgrek+RQmsEWjyml3ANsC1zVk2d1TZvUqI2+Psio78PU8Y/23R5G5b0ZlTinMtt9mG4ASRvQkiRJGnWnACcApT5+AngHkAF5y1QbGvXR10d9BPlp4x/xuz2Mwnsz6p8hWNh9sAu3JEmSRlopZW0p5ZFSyqPAqTzWTXs1sH1P1u2AuxY7PklLhw1oSZIkjbQky3tmDwQmRui+CDg4yWZJdqS5JeA1ix2fpKXDLtySJEkaGfWWgHsBz0yyGvgIsFeSFTTds28D3gVQSrkpyXnAzcB64GhH4JY0FzagJUmSNDJmckvAmv9E4MSFi0jShsQu3JIkSZIktWADWpIkSZKkFmxAS5IkSZLUgg1oSZIkSZJasAEtSZIkSVILNqAlSZIkSWrB21gBW5x+5wzXWAYr269z7xHbznD7kiRJkqSumfYMdJLPJFmX5MaetOOT3Jnk+jrt37PsuCSrktyaZJ+FClySJEmSpMXUpgv3GcC+A9JPLqWsqNMlAEl2Bg4Gdqnr/HWSjecrWEmSJEmShmXaBnQp5Urg7pbbOwA4t5TycCnl+8AqYPc5xCdJkiRJUifMZRCx9yS5oXbx3rKmbQvc0ZNndU2TpA3eJJfEbJXk0iTj9XHLmp4kn6qXxNyQZNfhRS5JkiSY/SBipwAnAKU+fgJ4B5ABectUGxofH2+5bNmMg+yKqfZxKb1mW12ODbodX5djg9nHNzY2Ns+RdNYZwF8CZ/WkHQtcVko5Kcmxdf4DwH7AWJ1eRVPuvmpRo5UkSdLjzKoBXUpZO/E8yanAxXV2NbB9T9btgLum2tZkFefx8fHHL5vBqNdds9iNgyccuw7pcmzQ7fi6HBt0P74uKKVcmWSHvuQDgL3q8zOBK2ga0AcAZ5VSCnBVki2SLC+lrFmcaCVJktRvVl24kyzvmT0QmOiOeBFwcJLNkuxIc+bkmrmFKElL2jYTjeL6uHVN95IYSZKkjpn2DHSSc2jOjjwzyWrgI8BeSVbQdM++DXgXQCnlpiTnATcD64GjSymPLEzokrSkzeiSmPnu3j/77Y3u5TYLbabHtOuXbExn1OOH0dsHewFJ0sKbtgFdSjlkQPJpU+Q/EThxLkFJ0gZk7UTX7Nq7Z11Nn9ElMfNZcZ5Td/wRvtxmoc3kmI76JRGjHj8sjX2QJM2/uYzCLUmau4uAw+rzw4ALe9IPraNx7wHc5/XPkiRJwzXbUbglSTM0ySUxJwHnJTkSuB04qGa/BNgfWAU8BByx6AFLkiTpcWxAS9IimeSSGIC9B+QtwNELG5EkSZJmwi7ckiRJkiS1YANakiRJkqQW7MItSZIkaV5scfpc78awzDs6qNM8Ay1JkiRJUgs2oCVJkiRJasEu3JIkLYKZdWuceRfGe4/YdmYBSZKkGfMMtCRJkiRJLdiAliRJkiSpBRvQkiRJkiS1YANakiRJkqQWbEBLkiRJktSCDWhJkiRJklqwAS1JkiRJUgveB1qSJEkjI8lngF8D1pVSXlzTtgI+B+wA3Aa8tZRyT5IAfw7sDzwEHF5K+cYw4pYAtjj9zgXb9r1HbLtg29ZjPAMtSZKkUXIGsG9f2rHAZaWUMeCyOg+wHzBWp6OAUxYpRklLlA1oSZIkjYxSypXA3X3JBwBn1udnAm/pST+rNK4CtkiyfHEilbQU2YCWJEnSqNumlLIGoD5uXdO3Be7oybe6pknSrHgNtCRJkpaqDEgrU60wPj6+QKEsnuHuw7IhvvaGbT7f9w35ezA2NjblchvQkjRkSV5IM/jNhOcBHwa2AN4J/KCmf7CUcskihydJo2BtkuWllDW1i/a6mr4a2L4n33bAXVNtaLrKc9eNj48Pdx9WLtwgWZrafL3vQ/8MzYOF3Ae7cEvSkJVSbi2lrCilrAB2oxkp9oK6+OSJZTaeJWlSFwGH1eeHARf2pB+axh7AfRNdvSVpNjwDLUndsjfw3VLKvzd3X5Ek9UpyDrAX8Mwkq4GPACcB5yU5ErgdOKhmv4TmFlaraP6cPGLRA5a0pNiAlqRuORg4p2f+PUkOBb4OHFNKuWfQSvN9rdLst+e1b8PStaLB0a4AACAASURBVOvVuhbPbIzaPox6l8u2SimHTLJo7wF5C3D0wkYkaUNiA1qSOiLJpsCbgeNq0inACTQD3pwAfAJ4x6B157PiPKfrhrz2bWi61Hjy+jlJ0lLlNdCS1B37Ad8opawFKKWsLaU8Ukp5FDgV2H2o0UmSJG3gpm1AJ/lMknVJbuxJ2yrJpUnG6+OWNT1JPpVkVZIbkuy6kMFL0hJzCD3dt+tIshMOBG58whqSJElaNG3OQJ8B7NuXdixwWSllDLiszkNz9mSsTkfRdD+UJE0jyTLgTcDne5L/JMm3k9wAvB74vaEEJ0mSJKDFNdCllCuT7NCXfADN6IcAZwJXAB+o6WfVARuuSrLFxD355itgSVqKSikPAc/oS3v7kMKRJEnSALMdRGybiUZxvWH91jV9W+COnnyra9qkDeipRrh8/LLRHdl1GKN4dnnk0C7HBt2Or8uxwezjc6AeSZIkjYL5HoV70E1Ly1QrTFZxfsLolyM8sutiNw66PHJol2ODbsfX5dig+/FJkiRJczXbUbjXTgxuUx/X1fTVwPY9+bYD7pp9eJIkSZIkdcNsG9AXAYfV54cBF/akH1pH494DuM/rnyVJkiRJS8G0XbiTnEMzYNgzk6wGPgKcBJyX5EjgduCgmv0SYH9gFfAQcMQCxCxJkiRJ0qJrMwr3IZMs2ntA3gIcPdegJEmSJEnqmvkeREwDbHH6wg6Adu8R2y7o9iVJkiRJs78GWpIkSZKkDYoNaEmSJEmSWrABLUmSJElSCzagJUmSJElqwUHEloAnDlK2DFbOz8BlDlAmSZIkSQ3PQEuSJEmS1IINaEmSJEmSWrABLUmSJElSCzagJUmSJElqwQa0JEmSJEktOAq3JHVAktuAB4BHgPWllFck2Qr4HLADcBvw1lLKPcOKUZIkaUNnA1qSuuP1pZQf9swfC1xWSjkpybF1/gNzeYEn3vZukPm7FZ4kSdJSYhduSequA4Az6/MzgbcMMRZJkqQNnmegJakbCvDlJAX421LK3wHblFLWAJRS1iTZerKVx8fHW77MsrlHqk5q/xlYHF2LZzZGbR/GxsaGHYIkLXk2oCWpG/YspdxVG8mXJvnOTFZuXXG2a/aS1aXG0/j4eKfimY2lsA+SpPlnA1qSOqCUcld9XJfkAmB3YG2S5fXs83Jg3VCDVKe1u759du49YtsF27YkSaPEa6AlaciSPDXJ0yaeA78C3AhcBBxWsx0GXDicCCVJkgSegZakLtgGuCAJNOXy35dSvpjkWuC8JEcCtwMHDTFGSZKkDZ4NaEkaslLK94CXDUj/EbD34kckSZKkQezCLUmSJElSC56BliRJ0pKQ5DbgAeARYH0p5RVJtgI+B+wA3Aa8tZRyz7BilDTaPAMtSZKkpeT1pZQVpZRX1PljgctKKWPAZXVekmbFBrQkSZKWsgOAM+vzM4G3DDEWSSPOBrQkSZKWigJ8Ocl1SY6qaduUUtYA1MethxadpJHnNdCSJElaKvYspdyVZGvg0iTfmekGxsfHFyCsxTXcfVg2xNfesM3n+74hfw/GxsamXG4DWpIkSUtCKeWu+rguyQXA7sDaJMtLKWuSLAfWTbWN6SrPXTc+Pj7cfVh55/BeewM3X+/70D9D82Ah92FOXbiT3Jbk20muT/L1mrZVkkuTjNfHLecnVEmSJGmwJE9N8rSJ58CvADcCFwGH1WyHARcOJ0JJS8F8XAPtSIeSJEkatm2AlUm+BVwD/N9SyheBk4A3JRkH3lTnJWlWFqIL9wHAXvX5mcAVwAcW4HW0CLY4fb674Sx7XNeee4/Ydp63L0mSNkSllO8BLxuQ/iNg78WPSNJSNNcG9MRIhwX421LK39E30mEdxGFSU13c/fhlDkiwFHVxgIIuxjShy7HBwg3WIEmSJHXBXBvQcx7pcLKK8xMu/HZAgiWpaw2nLg+a0OXYoPvxSZIkSXM1p2uge0c6BB430iFAm5EOJUmSJEkaBbNuQDvSoSRJkiRpQzKXLtzbABckmdjO35dSvpjkWuC8JEcCtwMHzT1MSZIkSZKGa9YNaEc6lCRJkiRtSObjPtCSJEmSJC15C3EfaEnSDCTZHjgLeDbwKPB3pZQ/T3I88E7gBzXrB0splwwnSknSUrHF6d7dRpotG9CSNHzrgWNKKd+ogzNel+TSuuzkUsrHhxibJEmSKhvQkjRkpZQ1wJr6/IEktwDbDjcqSZIk9bMBLUkdkmQH4OXA1cCewHuSHAp8neYs9T2D1hsfH2/5CsvmIUptaNp/vua2TteM2j6MjY0NOwRJWvJsQEtSRyTZHDgfeF8p5f4kpwAnAKU+fgJ4x6B1W1ecV3rdm2Zupg2z8fHxkW/MLYV9kCTNPxvQGqqFHsTi3iPsBavRkORJNI3ns0spnwcopaztWX4qcPGQwpMkSRLexkqShi5JgNOAW0opn+xJX96T7UDgxsWOTZIkSY/xDLQkDd+ewNuBbye5vqZ9EDgkyQqaLty3Ae8aTniSJEkCG9CSNHSllJVABizyns+SJEkdYhduSZIkSZJasAEtSZIkSVILduHWkjbzUb6Xzeg2P47yLUmSJG04PAMtSZIkSVILNqAlSZIkSWrBBrQkSZIkSS14DbQkSZIkjbiZj/0zmcFjAjn2T8Mz0JIkSZIktWADWpIkSZKkFuzCLc3B/HWVeSK7yUiSJEndYgNakiRNaeZ/Fg6+fm4y/mEoSRoVduGWJEmSJKkFz0BLkiRJkqa0kJcuwuj0RvIMtCRJkiRJLdiAliRJkiSpBRvQkiRJkiS14DXQkiRpqLyuTpI0KhbsDHSSfZPcmmRVkmMX6nUkaSmzLJWkubMslTRfFuQMdJKNgb8C3gSsBq5NclEp5eaFeD1pKXriGZmZ3Vd1Op6R6T7LUkmau4UoS+01IW24UkqZ/40mrwaOL6XsU+ePAyil/DHAfffdN/8vKmlJePrTn55hx9AVlqWSZsuy9DGWpZJma1BZulBduLcF7uiZX13TJEntWZZK0txZlkqaNwvVgB70r6f/7knSzFiWStLcWZZKmjcLNQr3amD7nvntgLsmZuxWJEmtWJZK0txZlkqaNwt1BvpaYCzJjkk2BQ4GLlqg15KkpcqyVJLmzrJU0rxZkAZ0KWU98B7gS8AtwHmllJsG5U2yfZLLk9yS5KYk763pWyW5NMl4fdxyIWKdTpInJ7kmybdqfH9Q03dMcnWN73O1QB6KJBsn+WaSizsY221Jvp3k+iRfr2ldeW+3SPIPSb5TP3+v7lBsL6zHbGK6P8n7OhTf79Xvw41Jzqnfk8587paKmZSls9H18reNUSij2+hyOd5Gl8v6Nrr8e6C5m6wsXQplICyJ8mOkv3+jWCdK8pkk65Lc2JM28Jin8ak0t4C7Icmuw4v857EOiv9P62fohiQXJNmiZ9lxNf5bk+wz19dfsPtAl1IuKaXsVEp5finlxCmyrgeOKaW8CNgDODrJzsCxwGWllDHgsjo/DA8DbyilvAxYAeybZA/gY8DJNb57gCOHFB/Ae2l+ECZ0KTaA15dSVpRSXlHnu/Le/jnwxVLKLwEvozmGnYitlHJrPWYrgN2Ah4ALuhBfkm2B/wG8opTyYmBjmn/zu/a5WxJmUJbORtfL3zZGoYxuo+vleBtdLevb6OzvgebHJGXpUigDYfTLj5H9/o1wnegMYN++tMmO+X7AWJ2OAk5ZpBincgZPjP9S4MWllJcC/wYcB1C/0wcDu9R1/jrNre1mr5TSqQm4kOY+fbcCy2vacuDWDsS2DPgG8Crgh8AmNf3VwJeGFNN2NB/yNwAX0wyU0YnY6uvfBjyzL23o7y3wC8D3qbdy61JsA2L9FeBfuhIfj41muhXNOAoXA/t06XPnNOv3trPlb8v4O1dGt4y70+V4y33oZFnfMvaR+T1wWvDPwsiVgaNefoz692+U60TADsCN0x1z4G+BQwbl61L8fcsOBM6uz48DjutZ9iXg1XN57QU7Az0bSXYAXg5cDWxTSlkDUB+3HmJcGye5HlhH8+/Gd4F7S9MlCIZ7O4Q/A34feLTOP4PuxAbNKJdfTnJdkqNqWhfe2+cBPwBOr92ePp3kqR2Jrd/BwDn1+dDjK6XcCXwcuB1YA9wHXEe3Pneaoa6Wv210vIxuo+vleBtdLevbGKXfAy2QES4DR738GOnv3xKrE012zEfxNnDvAP6xPp/3+DvTgE6yOXA+8L5Syv3DjqdXKeWR0nSl3Q7YHXjRoGyLGxUk+TVgXSnlut7kAVmHeauGPUspu9J0/zg6yeuGGEuvTYBdgVNKKS8HfkwHuwfVa2beDPyfYccyoV4TcwCwI/Ac4Kk0728/bxEyIrpc/rbR1TK6jREpx9voalnfxkj8HmjhjGoZuETKj5H+/m0gdaKR+kwl+RDN5RlnTyQNyDan+DvRgE7yJJqC6+xSyudr8toky+vy5TRnFoaqlHIvcAXNdTJbJJm4DdjjboewiPYE3pzkNuBcmu47f9aR2AAopdxVH9fRXMO7O914b1cDq0spV9f5f6ApwLsQW6/9gG+UUtbW+S7E90bg+6WUH5RS/hP4PPDLdOhzp/ZGpfxto4NldBudL8fb6HBZ38ao/B5oAYx4GbgUyo9R//4tpTrRZMd8ytvAdUmSw4BfA/5bqf21WYD4h96AThLgNOCWUsonexZdBBxWnx9Gc13KokvyrIlR3JI8heaLcgtwOfCbw4yvlHJcKWW7UsoONN18/6mU8t+6EBtAkqcmedrEc5preW+kA+9tKeU/gDuSvLAm7Q3c3IXY+hzCY923oRvx3Q7skWRZ/f5OHLtOfO7UXtfL3za6XEa30fVyvI0ul/VtjNDvgebZqJeBS6H8WALfv6VUJ5rsmF8EHFpH494DuG+iq3eXJNkX+ADw5lLKQz2LLgIOTrJZkh1pBkO7Zk4vNt8XdM90Al5Dcxr9BuD6Ou1Pcw3HZcB4fdxqSPG9FPhmje9G4MM1/Xn14K+i6V672ZCP417AxV2KrcbxrTrdBHyopnflvV0BfL2+t18AtuxKbDW+ZcCPgKf3pHUiPuAPgO/U78Rngc268rlzmtH72Onyt+U+jEQZ3XJfOleOt4y702V9y33o9O+B04K97yNfBvbsy0iWHzXekf7+jWKdiObkzBrgP2nO0B452TGn6QL9VzTji3ybZsTxLsa/iuZa54nv8t/05P9Qjf9WYL+5vn7qRiVJkiRJ0hSG3oVbkiRJkqRRYANakiRJkqQWbEBLkiRJktSCDWhJkiRJklqwAS1JkiRJUgs2oCVJkiRJasEGtCRJkiRJLdiAliRJkiSpBRvQkiRJkiS1YANakiRJkqQWbEBLkiRJktSCDWhJkiRJklqwAS1JkiRJUgs2oCVJkiRJasEGtCRJkiRJLdiAliRJkiSpBRvQkiRJkiS1YANakiRJkqQWbEBLkiRJktSCDWhJkiRJklqwAS1JkiRJUgs2oCVJkiRJasEGtCRJkiRJLdiAliRJkiSpBRvQkiRJkiS1YANakiRJkqQWbEBLkiRJktSCDWhJkiRJklqwAS1JkiRJUgs2oCVJkiRJasEGtCRJkiRJLdiAliRJkiSpBRvQkiRJkiS1YANakiRJkqQWbEBLkiRJktSCDWhJkiRJklqwAS1JkiRJUgs2oCVJkiRJasEGtCRJkiRJLdiA1lAk2StJSbLdoPmW2zg+yaqFi1KSIMkZSb4y2fwoSPK7SVYneTTJ8R2IZ+SOoTTfNqTvQZIdaj3vNcOORcNXPwtvG3Ycs2UDWjOS5G1JygJs+l+B5cBdC7DtKSX5dJIrFvt1JY2s9wIHDTuItpI8B/gz4I+BbYGPL+JrT/abMVLHUJI2BEnWJzl8Hrf3lSRnDFi0HPiH+XqdxbbJsAOQAEopPwP+Y9hxSNJ0Sin3zXUbSTYCUkp5ZB5Cms7zaP4wv6iUsmYRXm9a83EMJUmLL0mATUop/znbbZRSRrrO7xnoEZLkSUlOSnJnkp8luTnJb/csL0n+e5LPJnkgyR1Jfr9vGwck+WaSh5Lcm+SaJC/vWf6CJOfXZfck+XKSl9RlewGf7XmtMsm/SoNin+g++FCSLwHP7Vve36U7SU5N8t0kP0nyvSR/lGSzAdv+7br8p/Wfrh37lr8pyb/U7dyZ5PQkz6jLjgeOBP5Lzz4dXpdtnuTP6zoP1eP2633b/mB97YeT/CDJl5I8pc0xkTS/klyR5DO1nPxhkvtrD5On9OT53STfqeXFeJIPJdmkZ/mWST6X5MdJ1ib5KJC+1+nv0r1RLZ9+kOTBJOcmeV+S9T15jk+yKslvJfkO8DPgRXXZwUmurzHdluSTSZ7a95pTxj3FMTke+Oc6e3st43bIgEtgkrxmYnmdPzzN2Yg9k3yjloPXJtmtb73nJ/k/Se6ueW5I8mtT/WYMOIZJ8j9refqzWva/r+91bkvyh7Vcvru+Px9PsvF0x0EahjZlUl/+XZP8Y5J1tSy5Nsm+fXkmrcflsbrU/km+lqbec12SXeq0sq53TZKde7a5ZZL/neT2us6tSY5Jkv4Yp9jXQ+r39qdJ/rWWAZN22c4kXbprOXl8z/zmSf4sTZ324VoOfLBn+QuT/N96vB5M8v8leUHP8l9IU+/7j7r+HUk+2feasypfe9b/rXqcf5rkR/U93LIum7LuXvO0qb9vkuTD9Rg/XLf3F33HadI6a8/xfms9Rg/V8vbtPXluAzYGTq95S02f+C14fZJvAg8D+yTZMcnnk9xVt/ftvu2dAewNHJbHfgP26tnnt/XkXZ7mt/Pe+hm8IskrepZPfLbflOTK+no3J9mn7fs0r0opTiMyAX8K/Iim29tOwAeBR4G96/ICrAXeCTwf+B817fV1+bNpKm2/D+xIU3n7beAldfk2NGeBTwFeArwQ+Iv6ms8CNgWOrtt8dp2e3iLuA4D1wPtr3EfWOAuwXc2zV9/8RsBHgVcBOwBvBtYAf9Cz3eOBHwMrgVfW6WrgWzRndgDeADwE/C4wVvNcDlxJUyneHDibpgv5xD49pS67HLgCeA3NGZyj6vGbON6/DtwP/FeaPwRWAO8DnjLsz4qT04Y41e/r/cCptXz7r8A64FN1+fHAvwMH1jJwf+B24ISebVwArKplxy7A/67b/EpPnjP65t8PPAi8vZYz7wfuBtb35Dm+lkVfBfaoZeHTgMOBe+q6zwNeB9wAfLZv3SnjnuKYbF7LqgK8vJZxG9dtrurL+5qab4c6fzjNb8yVwGuBXwK+DHyX5uwDdXtrga/U9Z9PU+bvzxS/GQOO4dHAT2o5Owa8G/gpcGRPntvqsTq25vktmt+WI4b92XNyGjS1KJP6vwd7AYcBO9cy4qM09Y6d6vLp6nF71e/bN2nKsJ2Br9Uy5UqaxsyLaOpNV/e87rOBDwC71u2+jaZMa/XdAnarZcVHaeqOb6EpRwvwmppnh6nme7a1Cji+Pk89ht+r25woI99Zlz+Fpmy8rMawG03dbRWwac3zKZp64ato6mq/PLF+XX48syxf6/pHAP8J/K96vF9Kc4nKM+vyKevuNc+U9fea58z62Xl7zbMH8Hs9x2m6OuvE8f4e8FbgBcBJNGXoWM3zrDr/3vqZeHZNP7zGfC3N5+p5Ne9LaMrul9aYfreuP9HueDrN5+5zPPYbsGnPPr+tJ/6rgetr/C+p69zTcxz3qut8C9iX5jfgLOBeYItF/24Pu3BxavlGwTKaf3z+e1/6BcA/1eeFWij3LP8O8Mf1+cvpqRwNeI3jgav60kJTWXpfnX8bUGYY+0rg7L60jzNFA3qS7fweMN4XbwFe0JO2U017Y52/AjipbzvPrXlW1PlPA1f05dmLpvL29L70zwBf6Inn34AnDfvz4eTk9PPv+23Axj1pR9Wy86k0Ddh9+9Y5FLi3Pn9BLRve1LN8U+BOpm5A30lfZQs4lyc2oB8FntuX7zbg3X1pr6txbFnL/injbnFcnlC+0r4BXYBde/LsUdNeWOdPoPnj9amTvPbA34wBx/AO4E/68pwMfK/vWF3Ul+eLwDnD/uw5OQ2aWpRJj/seTLKNbwEfqs+nq8dNfNff0pN2UE37jZ60A2va5lO87p8Dl7bcz7OBf+5Lezdzb0DvXfO8YpLXPbKWj8/sSduG5s+4Q+v8hcAZk6w/H+Xr7cBfTrH9KevudX66+vvEb9NvTvG+T1dnnTje7+9ZvgnNHyXv6klbDxzet53D67qvbXE8LgRO7Zn/yqDjz+Mb0BPv8849yzejOXH24b7P9q/35Hl2Tdun7XdyviavgR4dL6CpyF3Zl/5V4Lie+ev7lt9JU5hA8w/kl4Abk1xKU7B/vpRyR13+SmC3JA/2beMpNP/0zNbOwDl9aSuBY6ZaKck7gd+h+dI/leaL3n/ZwQ9KKT/vhlhK+bckP6yv+RWafdojyXsGvMQYTzxeE15JrTj39WDaFBivz8+j+Zfw35N8meYf0C+UUh6Yar8kLahryuOvK/4Xmu/tK2jKsvPz+EGtNgaenORZNOUGND1SgGZ8hiTX0pzJfYIkvwA8B7iqb9HXgN/sS1tbSrm9Z91nAb8IfDJJ78BeE4XORDfEKeMupfxgUGzzZOIf/wl31sdtgFtpzvj8aynlx7N9gXoMt2Pw79t7kywrpTxU0wb9xu2I1F2TlUnP789Yy4Q/oDnL92yaes+TacoJmL4eN6H3O/sfPev2p20NPJhmTIbfBw6m+S4+GXgSzZnZNibqXL2+1nLdqewG3FNK+foky3cBbi6l/HAioZSyNsmtdRnAX9OUn6+gqad9EfhSKeXRmmfW5WuSrYHtaXrmDNK27g5T1993rY+TvU6bOusTXqeUsj7J2p7Xmc61vTNJlgEfpulZsby+3mY0Z8NnYhfgR6WUm3tiezjJ1Tz2Pg6K/z+SPDKD+OeNDejRU/rm05f2swH5NwIopTySZD+aL9obgd8ATkpyUCnl4prvMmBQY3OuA770xz2lJAcBf0XTVe+rNF2gDgJObLN6z/ONgI9Rr8PrM9UABhvR7PMrByz7GUAp5c4kvwS8nubH7n8BH0vyqgE/ZpKGo/8avoNoeo70u3tA3plsv00Z19/InPhD8L0MrnCsBl5Wn08V92w8yhP390mD8vVV/if2c6MBaXM16Pet36S/cdKImKqcOYOml9zvA9+nOZN6Lk3DpE09bkLv4E5lirSJ784xNA269wPfAB6g6WX3qzPYr5mWA4/Wx+nKof+fvXsPk60q7zz+/QmKnonh4AVEIILaGiWJiJcQb0FRAybjIRNJcIwCkugoGo1mvCbRhGA0UVEnahIFRKNcvMIYvCBCnJMRUC7KTdJHZOAAAspVEcjBd/7Yu6GoU929+5yuW/P9PE89XbX2ql3v3lW1er+111p7sfUOWn7nsXFVfSXJLwG/RXMW81+A85LsxV3bv7nt61Jj7D92h81r2xY9Zl2G17mjqm7tK/t7miE7r6c5Y/5T4D00XbeXasH3sUd//DCG/wH+05ke62i6gfxmX/kzgAu6rqQaZ1bVO6rqGTTJ6UHt4m/T/NJzRVWt67vN/QJ3O0CWNmnLhcBT+8r6H/d7BnBOVb23qs6qqlmaM9H9Hpzkzl9xkzwKeCBwUe82DdiedVU1d6b9dppfG3t9G1gN3HfA8+48g1RVt1XVl6vqDTRjNlbRjNORNB5P6muffoPmO34uTRe3h8/THtzBXW3pU+aenOQ+DD4oAe6cTfrK9nV67bFYoFV1NU3X5UfPE9OtbUyLxb0prgG27dtXu89XeQFnAU9N36RnPRb9n1FVN9H8WDDo/9sPes4+S9Novjbp+wPqPgP4UFWdWFXn0XRhfXhvhUWO4zbVM4AvV9URVXVO27NvKT0PL2TpbeDcceVD5wraM7o79NQ5C3hA72RSfS4Adk3yoJ51bEcznO/OY+Oquq6qjqmql9P8KPCbNGfNN6t9rapraNqu+SayWpZjd5ofNQCeO8/yTsesHQ06Jp7PM2iGaB5XVd+hGV/9qE1Y3wXAg3L3ie22Ap7M0vbTyHgGekpU1S1JPgAcmuRamoPB/Wh++XlOl3UkeQrNOIOv0jTKMzQD/49oq/wDzXiSL6SZefZymq48+wD/WlX/l+YXUYDnJ1kL/KwnEZ3Pe4BPJzkTOIlmnN2LF34KFwMHJ1kDnA/8Ds1EOP1uoZkt8E9pfqn6X8B53NWV6C+BryY5nGYChpvb7d4PeFVV/azdpv2S7EozicPNwNfbdXwuyRtpukNtQ3NgfWtVfSTJwTQ/Qp1JM4nBXjSTAt3ZBUXSyD0Q+GCS99MceB5KMx7rxiTvAN7RdnE7meZ/4K8Cj6+qN1bVuiQnts9/OU178Caa7/VC3gP8VZrZtc+kOUB7Lt3OyLwVOCLJDcAXaM4SPQbYp6peXlU/WSzubrtlI6fS/OB3aJIjaJLnQzZhPR8CXg6ckORtND8m7EpztuJLdP+f8bfAe5LM0nRLfRbwik2MSZok87VJP83Gk1xfDLyo/a5sAfw1PclHh+O4TXUx8OIkz6TpOvwSmkm3ru/4/PcC30ry1zRneH+Zu4bpDWwHq+pnSf4deEPbdm5J08vwtp5qX6e5isBxSV5H0w39ocBjquqjwKdojvOOS/I/aY4D391uw3EASQ6jScQvoDnr/SKacb+XLVP7+lfAh9uu0J+hOS58JnBsVf1oc4/d2321LskngQ8luS9N9/gHAE+pqvfT4Zi162vRtNnPTPIl4Pbe7vEDXAysSfJZmn36Opr35+oB63sEzVnyG2vjy199neZ/56eSHNLW+wuaoQQfXkLso1MjHnTtbdNvNN1a3knTMNxOk6j9957ldw7I7ym7c/A+zUHNSTRdl2+jGdvy97Qz4rV1HkYzGcS1PXX+Bdilp877aL4cP2eeiRkGxP6aNu6ftTEdwMKzcN8b+Cea7jM30TSSr6JnMhraSXBoJqm5tI3368Aj+l776e1r3kzTveSidhvmctf55QAAIABJREFUZpF9QLtfbmxjOLAtv1+7v3/Q7u8f0oydeVa7/L/RjJW8niaRP5+eGWO9efM22htN4nUkd816enP7eFVPnYO562z09TQzf76iZ/kDaeY3+GnbDv4tzY9vC00idq+23o9oDiKOpZlp9eaeOm+nb9KunmX70hwQ3dK2d+fSTpzSNe5F9svd2tee8pfSnDH4GfAlmvGPd05QRDNxzIa+5+zY1tmzp+xRNJPi3Nhuw3eA5/Us3+h/xoB9GOB/tu3tf7ZxvbbvtS8F/ryvbKNJIL15m5TbYm3SgO/Br7bHFT9rP++vZAnHcYO+6/RNDtiWzU0G+Mj28dY07d5NbZwfpEn0L13Ctr6Q5qz6bW179vvtazyhXb4zfZOGtW3Hv9G0t7M0x1V3TiLW1rk/zcmRq2iOxX4AvKln+aPbffKT9vZF7j657F/QHJ/9pG2j/o2NJy7b5Pa1ff6L2nbvtnb//SvtzNAscuze1lnw+L1nPYe2n4vbac58v69n+WLHrBvt/7a8f3/vTXOcfBvtMTcD/he05TvRjMn/afv+/BXNjzmn9dR5OM0Y8J/Q87+jf5tpxlAfS3NC6mft+/TEnuV7Mvj/2EaTno3iNnepH0mSplqS02iS1D+agFiOBB5XVU9YtLKkFWmS2qRRS/IS4CjggVV1w7jjkZaTXbglSdoMSR5Kc1mYU4E7aGYkfQmDJ2SUpBUnyZ/RtIHX0cwb8S7g0ybPWomcREybJckvJfnJArcXjTtGSRqyO2jGta0FzqFJnl9RVf847BdO8vRF2uCnDzsGSSvbIm3MW9pqv0bTffp7wDtohv+9dFwxLwfbV83HLtzaLEm2ZPDs2HOuLq+LLElDkeR+3H3W2n5XVDNZoiRtkiSPXGDxdVW1qZfTm2i2r5rPWBLoG2+80axd0kBbb731plyL9x7JtlTSfGxLu7MtlTSfQW2pXbglSZIkSerABFqSJEmSpA6mJoGenZ0ddwibzNjHZ5rjn+bYYfrjn1Yrab+vlG1ZKdsBbsskWinbsRJN8nszqbEZ19JMalwwubEtR1xTk0BLkiRJkjROJtCSJEmSJHVgAi1JkiRJUgcm0JIkSZIkddApgU5yaZLzkpyb5Ntt2QOSnJxktv27TVueJB9Isi7Jd5PsPswNkCRJkiRpFJZyBvqZVbVbVT2xffwm4JSqmgFOaR8D7APMtLeXAR9ermAlSZIkSRqXzenCvQY4ur1/NLBvT/nHq3E6sDrJ9pvxOpIkSRIASY5Mck2S8/vKX53k4iQXJPm7nvI3tz0jL07yW6OPWNJK0jWBLuCrSc5K8rK2bLuqugqg/bttW74DcHnPc9e3ZZIkSdLm+hiwd29BkmfSnMT5taraFXh3W/5YYH9g1/Y5H0qyxUijlbSibNmx3lOr6sok2wInJ/neAnUzoKzmq9z1YtZPWrsK1l7Rqe5Sfetptwxlvb0m9WLiXUxz7DDd8U9z7NA9/pmZmSFHojmrjxpOOzrnhoP8vVTScFXVN5Ls3Ff8CuCdVXVbW+eatnwNcGxb/oMk64AnA98cUbhaZv4f07h1SqCr6sr27zVJPk/T8FydZPuquqrtoj3XUK0Hdup5+o7AlfOtu/OB85CS5yXFsIlmZ2enNkGY5thhuuOf5thh+uOXJE2VRwFPT3IYcCvwZ1X1LZpekKf31FuwZ+Ry/3A9yT+ET2psi8e1aiyvP737a3wmNbbF4lrs+HXRBDrJfwHuVVU3t/efC/w1cCJwAPDO9u8J7VNOBF6V5Fjg14Eb57p6S5IkSUOwJbANsAfwJOD4JA9niT0jl/OH30n+IXlSY+sU1xBPqsHgz8BU768xmdTYliOuLmegtwM+n2Su/qeq6stJvkXTOB0MXAbs19Y/CXgesA64BThosyKUJEmSFrYe+FxVFXBmkp8DD2KJPSMlaTGLJtBVdQnwuAHlPwb2GlBewCHLEp0kSZK0uC8AzwJOS/Io4D7Aj2h6Rn4qyXuBh9JcZvXMsUUpaep1nURMkiRJGrskxwB7Ag9Ksh54G3AkcGR7aavbgQPakzoXJDkeuBDYABxSVXeMJ3JJK4EJtCRJkqZGVb1wnkV/OE/9w4DDhheRpHuSrteBliRJkiTpHs0EWpIkSZKkDkygJWkEktw3yZlJvpPkgiR/1ZbvkuSMJLNJjktyn7Z8q/bxunb5zuOMX5IkSSbQkjQqtwHPqqrHAbsBeyfZA3gXcHhVzQDXAwe39Q8Grq+qRwKHt/UkSZI0RibQkjQC1fhJ+/De7a1oLrvymbb8aGDf9v6a9jHt8r2SZEThSpIkaQBn4ZakEUmyBXAW8Ejgg8D3gRuqakNbZT2wQ3t/B+BygKrakORG4IE01zXdyOzs7MD7G1u1GVuwuIVfe/zrG5eVsh3gtkyiue2YmZkZcySStPKZQEvSiLTXHt0tyWrg88BjBlVr/w4621wDyoC7DpxnZ2cXPohee0XHaDfNch7AL7otU2KlbAe4LZNopWyHJE0Lu3BL0ohV1Q3AacAewOokcz9m7ghc2d5fD+wE0C7fGrhutJFKkiSplwm0JI1Akge3Z55Jcj/g2cBFwKnAC9pqBwAntPdPbB/TLv96Vc17BlqSJEnDZxduSRqN7YGj23HQ9wKOr6ovJrkQODbJ3wDnAEe09Y8APpFkHc2Z5/3HEbQkSUux+qjNGSq0auhDjaTNZQItSSNQVd8FHj+g/BLgyQPKbwX2G0FokiRJ6sgu3JIkSZIkdWACLUmSJElSBybQkiRJkiR1YAItSZIkSVIHJtCSJEmSJHVgAi1JkiRJUgcm0JIkSZIkdWACLUmSpKmR5Mgk1yQ5f8CyP0tSSR7UPk6SDyRZl+S7SXYffcSSVhITaEmSJE2TjwF79xcm2Ql4DnBZT/E+wEx7exnw4RHEJ2kFM4GWJEnS1KiqbwDXDVh0OPAGoHrK1gAfr8bpwOok248gTEkr1JbjDkCSJEnaHEmeD1xRVd9J0rtoB+Dynsfr27KrBq1ndnZ2WeNa7vUtp+HFtmpI6x2N+fbLpL6XkxoXTG5si8U1MzOz4HITaEmSJE2tJKuAtwLPHbR4QFkNKAMWP3BeitnZ2WVd33IaamxrrxjOekdk0H6Z1PdyUuOCyY1tOeIygZYkSdI0ewSwCzB39nlH4OwkT6Y547xTT90dgStHHqGkFcMx0JIkSZpaVXVeVW1bVTtX1c40SfPuVfVD4ETgJe1s3HsAN1bVwO7bktSFCbQkSZKmRpJjgG8Cj06yPsnBC1Q/CbgEWAd8BHjlCEKUtILZhVuSJElTo6peuMjynXvuF3DIsGOSdM/hGWhJkiRJkjowgZYkSZIkqQMTaEmSJEmSOjCBliRJkiSpAxNoSZIkSZI6MIGWJEmSJKmDzgl0ki2SnJPki+3jXZKckWQ2yXFJ7tOWb9U+Xtcu33k4oUuSJEmSNDpLOQP9GuCinsfvAg6vqhngemDuIvYHA9dX1SOBw9t6kiRJkiRNtS27VEqyI/DbwGHA65IEeBbw39sqRwNvBz4MrGnvA3wG+IckaS9kL0mSJEkTafVRVwwoXQVrB5Uv3Q0H7bAs69H4dEqggfcBbwDu3z5+IHBDVW1oH68H5j4NOwCXA1TVhiQ3tvV/NGjFs7OzHUNY1bHe0nWPYbJfY1imOXaY7vinOXboHv/MzMyQIxm/JDsBHwceAvwc+Oeqen+StwN/DFzbVn1LVZ3UPufNNL167gD+pKq+MvLAJUmSdKdFE+gkvwNcU1VnJdlzrnhA1eqwbCOdD5yX6VefzYphE83Ozk5tgjDNscN0xz/NscP0xz8EG4DXV9XZSe4PnJXk5HbZ4VX17t7KSR4L7A/sCjwU+FqSR1XVHSONWpIkSXfqcgb6qcDzkzwPuC/wizRnpFcn2bI9C70jcGVbfz2wE7A+yZbA1sB1yx65JE2RqroKuKq9f3OSi7ir584ga4Bjq+o24AdJ1gFPBr459GAlSZI00KIJdFW9GXgzQHsG+s+q6kVJPg28ADgWOAA4oX3Kie3jb7bLv+74Z0m6S3t1gscDZ9D8SPmqJC8Bvk1zlvp6muT69J6n9Q6V2Uhvd/mFu84PbzjM4q89/vWNy0rZDnBbJtHcdtjrR5KGr+sY6EHeCByb5G+Ac4Aj2vIjgE+0Z0uuo+mCKEkCkvwC8FngtVV1U5IPA4fSDHU5FHgP8FI2cTjMol3nhzgcpjeO5bBShgGslO0At2USrZTtkKRpsaQEuqpOA05r719C052wv86twH7LEJskrShJ7k2TPH+yqj4HUFVX9yz/CPDF9uHccJg5vUNlJEmSNAZLuQ60JGkTtZf/OwK4qKre21O+fU+13wXOb++fCOyfZKskuwAzwJmjileSJEkb25wu3JKk7p4KvBg4L8m5bdlbgBcm2Y2me/alwMsBquqCJMcDF9LM4H2IM3BLkiSNlwm0JI1AVa1l8LjmkxZ4zmHAYUMLSpKmUJIjgbnLrP5KW/b3wH8Fbge+DxxUVTe0y94MHAzcAfxJVX1lLIFLWhHswi1JkqRp8jFg776yk4FfqapfA/6Du64g81iaCW13bZ/zoSRbjC5USSuNCbQkSZKmRlV9g+ZKL71lX62qDe3D02kmXgRYAxxbVbdV1Q+AdQyYBFeSujKBliRJ0kryUuBL7f0dgMt7lq1vyyRpkzgGWpIkSStCkrfSTLz4ybmiAdVqvufPzs4uazzLvb7lNLzYVg1pvSuDn7HxWyyumZmZBZebQEuSJGnqJTmAZnKxvapqLkleD+zUU21H4Mr51rHYgfNSzM7OLuv6ltNQY1t7xXDWu0L4GRuv5YjLLtySJEmaakn2Bt4IPL+qbulZdCKwf5KtkuwCzABnjiNGSSuDZ6AlSZI0NZIcA+wJPCjJeuBtNLNubwWcnATg9Kr6H1V1QZLjgQtpunYfUlV3jCdySSuBCbQkSZKmRlW9cEDxEQvUPww4bHgRSbonsQu3JEmSJEkdmEBLkiRJktSBCbQkSZIkSR2YQEuSJEmS1IEJtCRJkiRJHZhAS5IkSZLUgQm0JEmSJEkdmEBLkiRJktSBCbQkSZIkSR2YQEuSJEmS1IEJtCRJkiRJHZhAS5IkSZLUgQm0JEmSJEkdmEBLkiRJktSBCbQkSZIkSR2YQEuSJEmS1MGW4w5Aku4JkuwEfBx4CPBz4J+r6v1JHgAcB+wMXAr8flVdnyTA+4HnAbcAB1bV2eOIfSlWH3XFMq5tFay9a303HLTDMq5bkiRp6TwDLUmjsQF4fVU9BtgDOCTJY4E3AadU1QxwSvsYYB9gpr29DPjw6EOWJElSLxNoSRqBqrpq7gxyVd0MXATsAKwBjm6rHQ3s295fA3y8GqcDq5NsP+KwJWniJDkyyTVJzu8pe0CSk5PMtn+3acuT5ANJ1iX5bpLdxxe5pJXABFqSRizJzsDjgTOA7arqKmiSbGDbttoOwOU9T1vflknSPd3HgL37yuzNI2kkHAMtSSOU5BeAzwKvraqbmqHOg6sOKKv5Ks/Ozg68v7FVHaKcTAtv12Sb5tj7uS2TZ247ZmZmxhzJaFTVN9ofInutAfZs7x8NnAa8kZ7ePMDpSVYn2X7uh0tJWioTaEkakST3pkmeP1lVn2uLr547mGu7aF/Tlq8Hdup5+o7AlfOte+7AeXZ2duGD6LXLOcnXaE1rcrDoezJF3JbJs1K2YxncrTdPksV685hAS9okJtCSNALtrNpHABdV1Xt7Fp0IHAC8s/17Qk/5q5IcC/w6cKNnTCRpyTa5N89ymOReDsOLbXp7Oo2Cn7HxWyyuxX6UXDSBTnJf4BvAVm39z1TV25LsAhwLPAA4G3hxVd2eZCuaS7U8Afgx8AdVdemiWyJJK9tTgRcD5yU5ty17C03ifHySg4HLgP3aZSfRXMJqHc1lrA4abbiSNFWWtTfPcpjk3gFDjW2KezqNgp+x8VqOuLqcgb4NeFZV/aTtfrg2yZeA1wGHV9WxSf4ROJhmYoaDgeur6pFJ9gfeBfzBZkUpSVOuqtYy+EwIwF4D6hdwyFCDkqSVw948kkZi0Vm420uo/KR9eO/2VsCzgM+05f2XXpm7JMtngL2ywCw5kiRJUldJjgG+CTw6yfq2B887geckmQWe0z6GpjfPJTS9eT4CvHIMIUtaQTqNgU6yBXAW8Ejgg8D3gRuqakNbpffyKndO1lBVG5LcCDwQ+NGgdXfvGz+88RSj6J8/qWMAupjm2GG645/m2KF7/JPYxUeSNJmq6oXzLLI3j6Sh65RAV9UdwG5JVgOfBx4zqFr7d0mTNXQ+cB7ieIphH7xP6hiALqY5dpju+Kc5dpj++CVJkqR+i3bh7lVVN9BcV28PYHWSuQS8d0KGOydraJdvDVy3HMFKkiRJkjQuiybQSR7cnnkmyf2AZwMXAacCL2ir9U/WcEB7/wXA19vuM5IkSZIkTa0uXbi3B45ux0HfCzi+qr6Y5ELg2CR/A5xDc31T2r+fSLKO5szz/kOIW5IkSZKkkVo0ga6q7wKPH1B+CfDkAeW3ctd1TCVJkiRJWhGWNAZakiRJkqR7KhNoSZIkSZI6MIGWJEmSJKkDE2hJkiRJkjowgZYkSZIkqQMTaEmSJEmSOuhyHWhJkiRJ0mZafdQVy7i2VbD2rvXdcNAOy7huzccEWpIkSboHedLauydekrozgZbuoZb3F9CNfetpQ129JEmSNHKOgZYkSZIkqQMTaEmSJEmSOjCBliRJkiSpAxNoSZIkSZI6MIGWJEnSipDkT5NckOT8JMckuW+SXZKckWQ2yXFJ7jPuOCVNLxNoSZIkTb0kOwB/Ajyxqn4F2ALYH3gXcHhVzQDXAwePL0pJ084EWpIkSSvFlsD9kmwJrAKuAp4FfKZdfjSw75hik7QCmEBLkiRp6lXVFcC7gctoEucbgbOAG6pqQ1ttPbDDeCKUtBJsOe4AJEmSpM2VZBtgDbALcAPwaWCfAVVrvnXMzs4ua0zLvb7ls2rcAWgIJu3zNmnxzFksrpmZmQWXm0BL0ogkORL4HeCadnweSd4O/DFwbVvtLVV1UrvszTRj9e4A/qSqvjLyoCVpejwb+EFVXQuQ5HPAU4DVSbZsz0LvCFw53woWO3BeitnZ2WVd37Jae8W4I9AQTNLnbVI//8sRl124JWl0PgbsPaD88Krarb3NJc+PpZn8Ztf2OR9KssXIIpWk6XMZsEeSVUkC7AVcCJwKvKCtcwBwwpjik7QCmEBL0ohU1TeA6zpWXwMcW1W3VdUPgHXAk4cWnCRNuao6g2aysLOB82iOc/8ZeCPwuiTrgAcCR4wtSElTzy7ckjR+r0ryEuDbwOur6nqaSW5O76njxDeStIiqehvwtr7iS/AHSEnLxARaksbrw8ChNJPaHAq8B3gpkAF1O018s/DkGNM7ccykTkbSxTTH3s9tmTxz2zGJ4w0laaUxgZakMaqqq+fuJ/kI8MX24Xpgp56qnSa+WXRyjCmeOGZak4NJnUhlU7gtk2elbIckTQvHQEvSGCXZvufh7wLnt/dPBPZPslWSXYAZ4MxRxydJkqS7eAZakkYkyTHAnsCDkqynGae3Z5LdaLpnXwq8HKCqLkhyPM0MshuAQ6rqjnHELUmSpIYJtCSNSFW9cEDxvLPBVtVhwGHDi0iSJElLYRduSZIkSZI6MIGWJEmSJKkDE2hJkiRJkjowgZYkSZIkqQMTaEmSJEmSOnAWbknSVFh91BVDXf8NB+0w1PVLkqTp5xloSZIkSZI6WDSBTrJTklOTXJTkgiSvacsfkOTkJLPt323a8iT5QJJ1Sb6bZPdhb4QkSZIkScPW5Qz0BuD1VfUYYA/gkCSPBd4EnFJVM8Ap7WOAfYCZ9vYy4MPLHrUkSZIkSSO2aAJdVVdV1dnt/ZuBi4AdgDXA0W21o4F92/trgI9X43RgdZLtlz1ySZIkSZJGaEljoJPsDDweOAPYrqqugibJBrZtq+0AXN7ztPVtmSRJkiRJU6vzLNxJfgH4LPDaqropybxVB5TVfJVnZ2c7RrCqY72l6x7DZL/GsExz7DDd8Q839uF9p+Z0jX9mZmbIkUiSJEmbr1MCneTeNMnzJ6vqc23x1Um2r6qr2i7a17Tl64Gdep6+I3DlfOvufOC8dniXLxn2wfvs7OzUJgjTHDtMd/xDj32I36k507rvJUmSpEG6zMId4Ajgoqp6b8+iE4ED2vsHACf0lL+knY17D+DGua7ekiRJ0rAkWZ3kM0m+115B5jfmu3KMJG2KLmOgnwq8GHhWknPb2/OAdwLPSTILPKd9DHAScAmwDvgI8MrlD1uSJEnayPuBL1fVLwOPo5n8dr4rx0jSki3ahbuq1jJ4XDPAXgPqF3DIZsYlSZIkdZbkF4FnAAcCVNXtwO1J1gB7ttWOBk4D3jj6CCWtBJ0nEZMkSZIm2MOBa4GjkjwOOAt4DX1Xjkmy7XwrWO7JOyd3ItPhTySq0Zu0z9ukxTNnsbgWm8PHBFqSJEkrwZbA7sCrq+qMJO9nid21l3Pyy4meyHQEE4lq9Cbp8zapn//liGtJ14GWJEmSJtR6YH1VndE+/gxNQn11e8UY+q4cI0lLZgItSZKkqVdVPwQuT/Lotmgv4ELmv3KMJC2ZXbglSZK0Urwa+GSS+9BcFeYgmhNGxyc5GLgM2G+M8UmacibQkiRJWhGq6lzgiQMWbXTlGEnaFHbhliRJkiSpAxNoSZIkSZI6sAu3JEmSJE251UcN9/JkNxy0w1DXPy08Ay1JI5LkyCTXJDm/p+wBSU5OMtv+3aYtT5IPJFmX5LtJdh9f5JIkSQITaEkapY8Be/eVvQk4papmgFPaxwD7ADPt7WXAh0cUoyRJkuZhAi1JI1JV3wCu6yteAxzd3j8a2Len/OPVOB1YnWT70UQqSZKkQUygJWm8tquqqwDav9u25TsAl/fUW9+WSZIkaUycREySJlMGlNV8lWdnZwfe39iqzQhpZVt4v03uukfNbZk8c9sxMzMz5kgkaeUzgZak8bo6yfZVdVXbRfuatnw9sFNPvR2BK+dbydyB8+zs7MIH0WuHO0PnNBtW8rHoezJF3JbJs1K2Q5KmhV24JWm8TgQOaO8fAJzQU/6SdjbuPYAb57p6S5IkaTw8Ay1JI5LkGGBP4EFJ1gNvA94JHJ/kYOAyYL+2+knA84B1wC3AQSMPWJIkSXdjAi1JI1JVL5xn0V4D6hZwyHAjkiRJ0lLYhVuSJEmSpA5MoCVJkiRJ6sAEWpIkSZKkDkygJUmSJEnqwEnEJEmSJEkLWn3UFUuovQrWLqU+3HDQDksLaEw8Ay1JkiRJUgcm0JIkSVoxkmyR5JwkX2wf75LkjCSzSY5Lcp9xxyhpeplAS5IkaSV5DXBRz+N3AYdX1QxwPXDwWKKStCKYQEuSJGlFSLIj8NvAR9vHAZ4FfKatcjSw73iik7QSOImYJEmSVor3AW8A7t8+fiBwQ1VtaB+vB+adqWh2dnZZg1nu9S2fVeMOQNrIqL4vi73OzMzMgstNoCVJkjT1kvwOcE1VnZVkz7niAVVrvnUsduC8FLOzs8u6vmW1xNmRpVEYxfdlOb6XJtCSJElaCZ4KPD/J84D7Ar9Ic0Z6dZIt27PQOwJXjjFGSVPOMdCSJEmaelX15qrasap2BvYHvl5VLwJOBV7QVjsAOGFMIUpaAUygJUmStJK9EXhdknU0Y6KPGHM8kqaYXbglSZK0olTVacBp7f1LgCePMx5JK4dnoCVJkiRJ6mDRBDrJkUmuSXJ+T9kDkpycZLb9u01bniQfSLIuyXeT7D7M4CVJkiRJGpUuZ6A/BuzdV/Ym4JSqmgFOaR8D7APMtLeXAR9enjAlSZIkSRqvRRPoqvoGcF1f8Rrg6Pb+0cC+PeUfr8bpNJcN2H65gpUkSZIkaVw2dQz0dlV1FUD7d9u2fAfg8p5669sySZIkSZKm2nLPwp0BZbXQE2ZnZzuuetXSo+moewyT/RrDMs2xw3THP9zYh/edmtM1/pmZmSFHIkmSpEm2+qgrhrbuGw5avnO6m5pAX51k+6q6qu2ifU1bvh7YqafejsCVC62o84Hz2uHt0GEfvM/Ozk5tgjDNscN0xz/02If4nZozrftekiRJGmRTu3CfCBzQ3j8AOKGn/CXtbNx7ADfOdfWWJEmSJGmaLXoGOskxwJ7Ag5KsB94GvBM4PsnBwGXAfm31k4DnAeuAW4CDhhCzJEmSJEkjt2gCXVUvnGfRXgPqFnDI5gYlSdKoDW/s1SpYe8Wyjr+SJEnjsdyTiEmSNkGSS4GbgTuADVX1xCQPAI4DdgYuBX6/qq4fV4ySJEn3dJs6BlqStPyeWVW7VdUT28dvAk6pqhnglPaxJEmSxsQEWpIm1xrg6Pb+0cC+Y4xFkiTpHs8EWpImQwFfTXJWkpe1ZdvNXcmg/bvt2KKTJEmSY6AlaUI8taquTLItcHKS7y3lybOzswPvb2zVJoanzbXw+zI9Vsp2wMrZlrntmJmZGXMkkrTymUBL0gSoqivbv9ck+TzwZODqJNtX1VVJtgeume/5cwfOs7OzCx9Erx3WTNNazEpIbhb9fE2RlbItK2U7JGla2IVbksYsyX9Jcv+5+8BzgfOBE4ED2moHACeMJ0JJkiSBZ6AlaRJsB3w+CTTt8qeq6stJvgUcn+Rg4DJgvzHGKEkTLclOwMeBhwA/B/65qt7vJQElLScTaEkas6q6BHjcgPIfA3uNPiJJmkobgNdX1dltr56zkpwMHEhzScB3JnkTzSUB3zjGOCVNMbtwS5IkaepV1VVVdXZ7/2bgImAHvCSgpGVkAi1JkqQVJcnOwOOBM/CSgJKWkV24JUmStGIk+QXgs8Brq+qmdn6JTpb70maTe6k0L2moe5bul/tc/KoZJtCSJElaEZLcmyZ5/mRVfa4tXvIlAZfD5lxibPVRXnJQWk6dL/fZgV24JUmSNPXSnGo+Arioqt7bs8hLAkpaNp6Ahi+pAAAgAElEQVSBliRJ0krwVODFwHlJzm3L3gK8Ey8JKGmZmEBLkiRp6lXVWmC+Ac9eElDSsrALtyRJkiRJHZhAS5IkSZLUgQm0JEmSJEkdmEBLkiRJktSBCbQkSZIkSR04C7ckSSOw+qgrhrbuGw7aYWjrliRJd/EMtCRJkiRJHZhAS5IkSZLUgQm0JEmSJEkdmEBLkiRJktSBCbQkSZIkSR2YQEuSJEmS1IGXsZIkacoN8xJZ4GWyJEma4xloSZIkSZI6MIGWJEmSJKkDE2hJkiRJkjpwDLQkSZK0BN3mHVgFa4c7P4Gk0fMMtCRJkiRJHQwtgU6yd5KLk6xL8qZhvY4krWS2pZK0+WxLJS2XoSTQSbYAPgjsAzwWeGGSxw7jtSRppbItlaTNZ1sqaTkNawz0k4F1VXUJQJJjgTXAhUN6PUlaiWxLNRHuGu85nWM6h3kda6/BPRVsSyUtm1TV8q80eQGwd1X9Ufv4xcCvV9WrAG688cblf1FJK8LWW2+dcccwKWxLJW0q29K72JZK2lSD2tJhjYEe1GjbOEnS0tiWStLmsy2VtGyGlUCvB3bqebwjcOWQXkuSVirbUknafLalkpbNsLpwbwn8B7AXcAXwLeC/V9UFy/5ikrRC2ZZK0uazLZW0nIYyiVhVbUjyKuArwBbAkTZSkrQ0tqWStPlsSyUtp6FdB7qqTqqqR1XVI6rqsC7PWewafUm2SnJcu/yMJDsvd9ybo0P8r0tyYZLvJjklycPGEecgXa+PmOQFSSrJE0cZ32K6xJ/k99v9f0GST406xvl0+Nz8UpJTk5zTfnaeN444B0lyZJJrkpw/z/Ik+UC7bd9NsvuoY5x287Wl095ezumwHQcmuTbJue3tj8YR52JW0nehw7bsmeTGnvfkL0cdYxdJdmrbzovadv81A+pMxfvScVum4n0Zl005Ll2qJJcmOa/d/99uyx6Q5OQks+3fbYbx2gvE9Oiez8S5SW5K8tokb09yRU/5SI4tBrUv8+2jUX4/54nr75N8r33tzydZ3ZbvnORnPfvuH0cc17zvXZI3t/vr4iS/NeK4juuJ6dIk57blo9xfA9vKZf+MVdVE3Gh+Efw+8HDgPsB3gMf21Xkl8I/t/f2B48Yd9xLjfyawqr3/ikmJv0vsbb37A98ATgeeOO64l7jvZ4BzgG3ax9uOO+4lxP7PwCva+48FLh133D2xPQPYHTh/nuXPA75EM4HLHsAZ4455Jdymvb1c4nYcCPzDuGPtsC0r5rvQYVv2BL447jg7bMf2wO7t/fvTdOHt/3xNxfvScVum4n1ZyTfgUuBBfWV/B7ypvf8m4F1jjG8L4IfAw4C3A382hhg2al/m20ej/H7OE9dzgS3b++/qiWvn+drHEcU18L2jOUb8DrAVsEv7/3WLUcXVt/w9wF+OYX8NbCuX+zM2tDPQm+DOa/RV1e3A3DX6eq0Bjm7vfwbYK8mkXKZh0fir6tSquqV9eDrNJBaToMu+BziU5gN46yiD66BL/H8MfLCqrgeoqmtGHON8usRewC+297dmgiY+qapvANctUGUN8PFqnA6sTrL9aKJb0aa9vZzTte2ZeCvpu9BhW6ZCVV1VVWe3928GLgL6L6o8Fe9Lx23RZOpti48G9h1jLHsB36+q/zeuAOZpX+bbRyP7fg6Kq6q+WlUb2odjOW5fYnu8Bji2qm6rqh8A62j+z440rvZY4/eBY4bx2gtZoK1c1s/YJCXQOwCX9zxez8b/HO6s036gbwQeOJLoFtcl/l4H0/ziMQkWjT3J44GdquqLowysoy77/lHAo5L8e5LTk+w9sugW1iX2twN/mGQ9cBLw6tGEtiyW+r1QN9PeXs7p+vn4vbZr1WeS7DRg+TRYad+F30jynSRfSrLruINZTJohDI8HzuhbNHXvywLbAlP2vqxABXw1yVlJXtaWbVdVV0FzcA9sO7bomt5IvUnNq9q29chRdy3vM98+mqTv50u5+3H7LmmG1v1bkqePIZ5B792k7K+nA1dX1WxP2cj3V19buayfsUlKoLtco2+Sr+PXObYkfwg8Efj7oUbU3YKxJ7kXcDjw+pFFtDRd9v2WNN249wReCHx0bizLmHWJ/YXAx6pqR5quJp9o35NpMMnf2Wk27e3lnC4x/m9g56r6NeBr3PUL8rSZhvejq7OBh1XV44D/BXxhzPEsKMkvAJ8FXltVN/UvHvCUiX1fFtmWqXpfVqinVtXuwD7AIUmeMe6A5iS5D/B84NNt0YeBRwC7AVfRdLmdNBPx/UzyVmAD8Mm26Crgl6rq8cDrgE8l+cX5nj8E8713E7G/aI5be3+oGfn+WqStvFvVAWWL7rNJOgjvco2+O+ukuSTB1kxON7NO1xhM8mzgrcDzq+q2EcW2mMVivz/wK8BpSS6lGSNwYiZnIrGun50Tquo/224tF9Mk1OPWJfaDgeMBquqbwH2BB40kus3ntTeHY9rbyzmLbkdV/binrfwI8IQRxbbcVsx3oapuqqqftPdPAu6dZCLbpCT3pjmI+mRVfW5Alal5Xxbblml6X1aqqrqy/XsN8Hma7rNXz3UJbf+OawjZPsDZVXV1G+PVVXVHVf2cpm0dSlffjubbR2P/fiY5APgd4EXVDpptu0j/uL1/Fs1Y40eNKqYF3rtJ2F9bAv8NOG6ubNT7a562clk/Y5OUQH8LmEmyS/sr2f7AiX11TgQOaO+/APj63Id5Aiwaf9sN+p9okudJGYMLi8ReVTdW1YOqaueq2plmHMjzq+rb4wl3I10+O1+gmcSN9oDiUcAlI41ysC6xX0Yzbokkj6FJoK8daZSb7kTgJe0sh3sAN851odFmmfb2ck6XdrN3LNLzacYzTaMV811I8pC58fRJnkxzLPHj8Ua1sTbGI4CLquq981Sbively7ZMy/uyUiX5L0nuP3efZgKq87l7W3wAcMJ4Irz7WcG+tvV3aWIdl/n20Vi/n+1wvzfSHPPe0lP+4CRbtPcfTnNCZmTHlAu8dycC+6e5CscubVxnjiqu1rOB71XV+rmCUe6vBdrK5f2M1QhmROt6o+me+h80v0y8tS37a5oPLjSJw6dpBsWfCTx83DEvMf6vAVcD57a3E8cdc9fY++qexgTNwt1x3wd4L3AhcB6w/7hjXkLsjwX+nWZmxXOB54475p7Yj6HpmvOfNL/iHQz8D+B/9Oz3D7bbdt6kfW6m+Tbt7eUStuNvgQvaz/+pwC+PO+Z5tmPFfBc6bMuret6T04GnjDvmebbjaTRd8b7b83/3edP4vnTclql4X1bqjeZqAt9pbxf0tGcPBE4BZtu/DxhDbKtofkzZuqfsE+1n/rs0ScT2I4plUPsycB+N8vs5T1zraMbHzn3n5q5s8Xs937Wzgf864rjmfe9oerl+n6an5T6jjKst/9hcm9RTd5T7a762clk/Y2mfLEmSJEmSFjBJXbglSZIkSZpYJtCSJEmSJHVgAi1JkiRJUgcm0JIkSZIkdWACLUmSJElSBybQkiRJkiR1YAItSZIkSVIHJtCSJEmSJHVgAi1JkiRJUgcm0JIkSZIkdWACLUmSJElSBybQkiRJkiR1YAItSZIkSVIHJtCSJEmSJHVgAi1JkiRJUgcm0JIkSZIkdWACLUmSJElSBybQkiRJkiR1YAItSZIkSVIHJtCSJEmSJHVgAi1JkiRJUgcm0JIkSZIkdWACLUmSJElSBybQkiRJkiR1YAItSZIkSVIHJtCSJEmSJHVgAi1JkiRJUgcm0JIkSZIkdWACLUmSJElSBybQkiRJkiR1YAItSZIkSVIHJtCSJEmSJHVgAi1JkiRJUgcm0JIkSZIkdWACLUmSJElSBybQkiRJkiR1YAItSZIkSVIHJtCSJEmSJHVgAq2JkqSS/OG4XzfJpUn+fNRxSBqtcbU5krSSJNm5bU+fNu5YNleXY8AkH0vytSWs87QkH9386DQJthx3AFKf7YEbxh0E8CTglnEHIWn5tAc766vqwJ7iSWlz1GoPXP+oqnYedyzSPd087eYgl9O0pz8eelDLZDPbmtfgich7LBNo3U2S+1TV7eN6/ar64bheu1dVXTvuGCQN36S0OcstSYAtq+o/xx2LpJWt59hxRbang1TVjeOOQePjLydTLsnTkvx7kpvb23eS/Fa7bLu2i8m17bJ/T/KMnufu2Xa3+e0ka5PcCrwsyYFJNvS9zo5t3T37nvu8JN9M8rMkZyXZtb2tTXJLkjOTPHYJ29PflbqSvDLJJ9ptuDzJG/qesybJOe3r3dC+5uP74tyx7zkbkhy4QBx3677TPv7rJO9Pcl2Sq5O8O8kWXbdN0vgk+RiwF3BA2yZUT/vQ3+a8OslxSX6a5LIkL0iydZJPtu3QJUl+r2/9i7W3907y3iTrk9yW5Kokx3aM/cC2zXp2kguS3Nq2c7sPqPPMJOcAtwFz/wue08bzsyRXJDkqyQN7nnuvJO9oY/9JkmOTvLb3/0CStydZ17a332v3zalJHtFTZ5sk/9Lus58luTjJ65Ok931I8rUkL0vy/5LclOSEJA+e2w7gUOBhPe/T27vsJ0nLa55288D274uSnJTkp8A7MqALd5Jt2/bm6rbdujjJS3uWPzLJZ9tjt+uTfDXJr/Ys79L2/WL7Gj9s29bLk7y3w7YdyMJtzX0WOubLgC7cSf4gzbHwrUl+nORLSbaZ5/V3S3JlksPTmNvWpyY5O80x7beSPKHveYvtswX3RxbIG9SdCfQUa7/IJwJnALu3t7cDtyS5H3AqcH9gH+DxwEnAyUke07eq9wB/BzwG+MISwzgMeCvwBOB24Bjgw8DbesqOWuI6+70N+AawG/D3wLuSPBMgyUOAT7evuyvwG8D7gA2DV7VZXg1cBfw68CfAa4GXDOF1JC2/1wD/Bzieppvh9sD/nafuW2nay8cBXwQ+DhwLnEzTlv4r8PG5JLRje/tq4PeBPwRmgOcDpy8h/nvRtNOvBJ4MXAP8a5JVA+q8Hvhl4IwkzwJOaOP/NWBfYGfg8z2J7Wtp2rTXtbGfCfzlgBi2B14BvAh4CrAaOLJn+VbAee1rPJbm4PSvgAP71vMk4JnAbwN707Tt726XHQe8C1jPXe/Tu5E0Dgu1m+8CPgX8KvDB/ie27eK/0bSjL6JpE15NOzwuyXbAWpq27OnAHsDFwGlzP6i1Fmv7/obm+HcNTdv6B8BFHbZtsbZmScd8SQ4C/oXmOHp3mjbuy8BGJ1qS7AWcBryvqv60qqpnW/+WZr/vDlwPHJ9ky/Z5XfbZvPtjobxhwT2ljVWVtym9AdsABew5YNmBNI3Cln3lX6f5wgLs2T7/xQOeu6GvbMfe1+p57r49dfZry36vp+x327Jf6LhNBfxh3+MP9NX5HvC37f3Ht3V2nmd9c3Hu2Fe+AThwgde9FPjzvscn9q3jy8Ax4/4cePPmrdsN+Brwsb6yQW3O+3oeP7gt+189ZXNt7++0j7u0t+9vH2cT4j6wfb29+mL4Cc34vd46T+977mnAO/vKfqmtu1v7+Arg0L46x/b+H6A5yNoAPLinbH/g58B9F4j9/cDJPY8/BlwLbNVT9ibgqp7Hfw5cOu7Pizdv3jZuN2l+gCvgL/rqzZU/rX18MHBr//FXT/23A6f3lQX4PvDa9nGXtu+E/nZ9Cds2sK3pcszXtmVf63l8GfAPC7zWacBHgRe28Q869i5g956yPdqyRy9hn827P1ggb/C2tJtjoKdYVV2fZka/ryT5Os0vfZ+vqotpfuF/CHBDT+85aM4Q/KxvVWduRhjf6bk/N/bluwPKtqVpMDbFuX2PrwC263mtrwDnJzmZpoH6XFVdvomvtdQ4dhnC60garzvbtaq6Nskd9LRrbdt7O027Bt3a26NozmCva9uqk4H/XUubc+KbfTFcRHNWp9e3+h4/CdgjyasGrG8mySXAQ9n4bPg3gRf0lV1Zd58f4gqag7dtgcuS3At4A01ivSNwX+DewP/rW89FVXVb33q2Q9I0WezY8QnAhVW1fp7lTwKekKT/2PB+NGdOey3U9n0I+GySJwKn0CS6X6mqn3fYhoV0PuZLsi2wE/DVRda5N3AQsKaqvjhgeXH34+or2r/b0Zxp7rLP5t0fi+QNWgIT6ClXVX+c5P3Ac4HnAIe2B0r3oumy8bsDntbfVeOnfY8HNTr3nieE3glqaoGyzRku0H+AWXPrq6o7kuxD06g8G/g94J1J9msbp7lt6R2Dt8UmxjNvHJJWlEETb/WX9X7/F21vq+rcJLvQtNPPpDkze2iSParqpk2MM32P76iqW/vK7kXTTfETA57/Q+7qXlgDlvcb1AbOvQY0XcffTNMV/GzgZuBPabpqL7ae/m2RNNn6jx0HWahduRdNgjfox73FJui6s72oqq8k+SWaOR/2pOlGfV6Svarqjg4xzmdTjvkWa0fPpzkr/8dJvjrgB9Sf98Xc38Yuus8W2x/z5Q1V9U+LxK4eHvyvAFV1flW9t6r2AY4AXgZ8G3g4cFNVreu7XbnIKq8BtmjHWszZfb7K41aNM6vqHVX1DJpf1A5qF1/T/n1oz1N2w4M16Z7odgaMR1sGndrbqvpJVX2+qv4EeCLNvBO/uYTX2WPuTpLVNOOcFxvr921g1wFxrWvjuRG4kmb+iIGvtQTPAL5cVUdU1TlVtY6NzyR1Maz3SdLSber38Sxg1/RN4trj2zRz11wxoG3qvxLKgm1fVV1XVcdU1ctpfrD7TTbunTPIsrQ1VXUNzTCexSbjWk/TTj6aZh6KrZb4Up322WL7Y568QUtgAj3F2pn43tXOqPewJL9BM6nAhcAngR/QTLTw3DSzI/56kjcn2XeRVZ9Jc+bgnUlmkuzN4Allxi7JU5L8Rbttv9ROzPBrNPsAYB1N98G3J/nlNLNDHk63sy2SVpYf0HR/e0SSByWZr2fNUi3a3ib5n2lmrd21PRP9UuAO4D86vkYBf5fkGe2Mqx+nOQP0qUWe95fAmnam193abd87yRHtJD/QTCT52ja+mSSvpTk7sdR28mJgzzQzgT8qyd/QTMCzVD8AHpLkN9r3adWiz5A0LHdrN5m/R2K/Y2iOv05MM4v2Lkn2SvIH7fJ/oElev5Dk6W27+bQkhyV5Ss96Fmz72vr/Lcmjk8zQTFj2E5oxyV22bbnamr8CXt4ekz6mbetf1e6zuzam+VF1T5ox4yf2tMNdLLrPFtofi+QNWgIT6On2U5pf94+lOQj7LM3siK9qu/H9Js2vVUe1yz9HM4Nh/3i0u6mq62gmOdiDZtzfX9CMa5tEN9KcOTkBmKWZEfaTNLO/UlUbaGYg3BY4h2amyLcyuJu6pJXtPcCPaMaYXQs8dTlW2rG9vYmma/M3aWaq/l2aCRe7jj37OfAW4J/a19ke+O2qWrAbZVWdCjyLZqbc/0PTph9O8yPpXLf099EcmL2fpp3cg2Zf9XcHX8yhND2ATqDZzm2ADyxxHdDMYvtpmtnOr2Vy//9I9wSb1G5W1S007eL5NMepF9Ecg92vXX41zfHbj2jay4tpjt8eRjP79ZzF2r5bgb+mOeP9bZqTKPtUt+s0L1tbU1UfpZkI7AU046e/QXNVho2uClNVP6RJoh8CfLFr4t5xny20P+bNG5a4ufd4qfJEnCRJkyrN9Uo/WlUjm7ckyZHA46rqCYtWlqQhGEfbJ3XhB1KSpHuwJA+lOSN+Kk238v9Kc71Tz0pIktTHBFojk+RLNGMtBvk/7WQGknSPMuCSJL3eQTPJ1zDdAexH0wX7vjRzR7yiqj4y5NeVpKFoZ6JeaGzvy6vqk6OKRyuLXbg1Mkl2oB37MsDPquqKeZZJ0oqV5JELLL6unZdCktRRki1pJuqaz9VVdfOIwtEKYwItSdL/b+/+gz2r6zvPP19DI9pqaNBA2u6uBdc7U5pUCYyDZJidImAMEMsmW2JwU9oiOyYzWKsTdyNmqqL5Ya3ORolusmSjgI2lAosydFEkkUEsq6sWVBARxMztICtNd+iMQItjxQR87x/fzzVfmu+999wf35/3+aj61vd7Pudzvvf9Oef2uZ93n8/5HEmSpA7GMoT78OHDZu2SBjr22GN9RndHnkslLcZzaXeeSyUtZtC51MdYSZIkSZLUgQm0JEmSJEkdTGwCPT8/P+4Qhs42zgbbqGkyScfSWAablFgmJQ4wlsVMUiwaj1n6HZiltsBstWeW2gJrb8/EJtCSNEuS7Ehye5IHktyf5J2t/P1JHklyT3ud37fNe5PsS/JXSX5pfNFLkiQJfA60JI3KU8C7q+ruJC8E7kpya1t3eVX9YX/lJK8ALgJ+FngJ8J+T/NOqenqkUUuSJOknvAItSSNQVQer6u72+UngAWDbEpvsBK6tqh9V1XeAfcDpw49UkiRJizGBlqQRS3IScCpwZyt6R5J7k1yV5LhWtg14uG+z/SydcEuSJGnIHMItSSOU5AXA54B3VdX3k1wB/D5Q7f3DwNuAQc9wXfRZpSuZEGOSJgMxlsEmJZZJiQOMZTH9sczNzY0xEknaGEygJWlEkhxNL3n+dFV9HqCqHu1b/3Hg5ra4H9jRt/l24MBi39214zw/Pz8xnWxjGWxSYpmUOMBYFjNJsUjSRuEQbkkagSQBrgQeqKqP9JVv7av2K8B97fMe4KIkxyQ5GZgDvjKqeCVJkvRsXoGWpNE4E3gz8M0k97Sy3wbelOQUesOzHwJ+HaCq7k9yPfAtejN4X+oM3JIkSeNlAg1sufqRoX7/Exc774+00VXVXgbf13zLEtt8APjAesbxL/Zuhr3DO+d5vpM0CkmOAr4GPFJVr2sjda4FjgfuBt5cVX+f5BjgGuCfA98DfrWqHhpT2BNjmH1f/w5o1jmEW5IkSdPmnfQeB7jgQ8DlVTUHPA5c0sovAR6vqpcBl7d6krRqJtCSJEmaGkm2A78MfKItBzgbuKFV2Q1c0D7vbMu09ee0+pK0Kp0S6CQPJflmknuSfK2VHZ/k1iTz7f24Vp4kH0uyrz3X9LRhNkCSJEkbyh8BvwX8uC2/CHiiqp5qy/uBhXHE24CHAdr6w62+JK3KSu6B/oWq+q99y5cBt1XVB5Nc1pbfA5xHb7bYOeDVwBXtXZIkSVq1JK8DDlXVXUnOWigeULU6rHuWSXrGd1eri3nzusexYC37cBr3/1JmqT2z1BZYuj3LPR5wLZOI7QTOap93A1+il0DvBK6pqgLuSLIlydaqOriGnyVJkiSdCbw+yfnAc4GfondFekuSTe0q83bgQKu/H9gB7E+yCTgWeGyxL5+252qv+lngQ5xMcrX7cNaeaz5L7ZmltsDa29P1HugCvpDkriRvb2UnLiTF7f2EVv6ToTJN/zAaSZIkaVWq6r1Vtb2qTgIuAr5YVb8G3A68oVXbBdzUPu9py7T1X2wXeSRpVbpegT6zqg4kOQG4Ncm3l6i7bkNlRjdUYHjDWGBS2jg+tnE2DLONs/S/mpKksXgPcG2SPwC+DlzZyq8EPpVkH70rzxeNKT5JM6JTAl1VB9r7oSQ3AqcDjy4MzU6yFTjUqi8MlVnQP4zmWRbrOI90qMAQh7HAhLRxTGzjbNgIbZQkTZeq+hK9Wwipqgfp9U+PrPN3wIUjDUzSTFt2CHeS5yd54cJn4LXAfTxzSMyRQ2Xe0mbjPgM47P3PkiRJkqRp1+UK9InAje2ReZuAz1TVXyT5KnB9kkuA7/KP/7t3C3A+sA/4IXDxukctSZIkSdKILZtAtyExrxxQ/j3gnAHlBVy6LtFJkiRJkjQhus7CLUmSJEnShmYCLUmSJElSBybQkiRJkiR1YAItSZIkSVIHJtCSJEmSJHVgAi1JkiRJUgcm0JIkSZIkdWACLUmSJElSBybQkiRJkiR1YAItSZIkSVIHJtCSJEmSJHVgAi1JkiRJUgcm0JIkSZIkdWACLUmSJElSBybQkiRJkiR1YAItSZKkqZHkuUm+kuQbSe5P8rut/JNJvpPknvY6pZUnyceS7Etyb5LTxtsCSdNs07gDkCRJklbgR8DZVfWDJEcDe5P8eVv3v1XVDUfUPw+Ya69XA1e0d0laMa9AS5IkaWpUzw/a4tHtVUtsshO4pm13B7AlydZhxylpNnkFWpIkSVMlyVHAXcDLgD+pqjuT/FvgA0l+B7gNuKyqfgRsAx7u23x/Kzt45PfOz88PPfb1trqYN697HAvWsg+ncf8vZZbaM0ttgaXbMzc3t+S2JtCSJEmaKlX1NHBKki3AjUl+Dngv8DfAc4A/A94D/B6QQV8x6HuX6zhPmvn5+dXFvPeR9Q+mWe0+XHVbJtQstWeW2gJrb49DuCVJkjSVquoJ4EvAuVV1sA3T/hFwNXB6q7Yf2NG32XbgwEgDlTQzTKAlSZI0NZL8dLvyTJLnAa8Bvr1wX3OSABcA97VN9gBvabNxnwEcrqpnDd+WpC4cwi1JkqRpshXY3e6D/ifA9VV1c5IvJvlpekO27wF+o9W/BTgf2Af8ELh4DDFLmhEm0JIkSZoaVXUvcOqA8rMXqV/ApcOOS9LG4BBuSRqBJDuS3J7kgST3J3lnKz8+ya1J5tv7ca08ST6WZF+Se5OcNt4WSJIkyQRakkbjKeDdVfVy4Azg0iSvAC4DbquqOdpjV1r984C59no7cMXoQ5YkSVI/E2hJGoE2O+zd7fOTwAP0nkO6E9jdqu2mN/ENrfyaNqPsHcCWhQlyJEmSNB4m0JI0YklOonf/3p3AiQuzwbb3E1q1bcDDfZvtb2WSJEkaEycRk6QRSvIC4HPAu6rq+72nrQyuOqCsFqs8Pz/fMYLNHeutTvc4Vld/mIzl2SYlDjCWxfTHMjc3N8ZIJGljMIGWpBFJcjS95PnTVfX5Vvxokq1VdbAN0T7UyvcDO/o23w4cWOy7O3ec9z6y0rBXZCUd+Pn5+Ynp8BvL5MYBxrKYSYpFkjYKh3BL0gikd6n5SuCBqvpI36o9wK72eRdwU1/5W9ps3GcAh+OvNoMAAB3OSURBVBeGekuSJGk8vAItSaNxJvBm4JtJ7mllvw18ELg+ySXAd4EL27pbgPOBfcAPgYtHG64kSZKOZAItSSNQVXsZfF8zwDkD6hdw6VCDkiRJ0op0HsKd5KgkX09yc1s+OcmdSeaTXJfkOa38mLa8r60/aTihS5IkSZI0Oiu5B/qd9J5buuBDwOVVNQc8DlzSyi8BHq+qlwGXt3qSJEmSJE21Tgl0ku3ALwOfaMsBzgZuaFV2Axe0zzvbMm39OVniOS2SJEmSJE2DrvdA/xHwW8AL2/KLgCeq6qm2vB/Y1j5vAx4GqKqnkhxu9f/roC9e6lmKo3vO4vieizpJz5IcFts4G4bZRh/DIkmSpGmwbAKd5HXAoaq6K8lZC8UDqlaHdc+yWMd5pM82HNNzUTfC8xtt42zYCG2UJEmSltPlCvSZwOuTnA88F/gpelektyTZ1K5CbwcOtPr7gR3A/iSbgGOBx9Y9ckmSJEmSRmjZe6Cr6r1Vtb2qTgIuAr5YVb8G3A68oVXbBdzUPu9py7T1X2yPY5EkSZIkaWqtZBbuI70H+M0k++jd43xlK78SeFEr/03gsrWFKEmSJEnS+HWdRAyAqvoS8KX2+UHg9AF1/g64cB1ikyRJkp4hyXOBLwPH0OvL3lBV70tyMnAtcDxwN/Dmqvr7JMcA1wD/HPge8KtV9dBYgpc09dZyBVqSJEkatR8BZ1fVK4FTgHOTnAF8CLi8quaAx4FLWv1LgMer6mXA5a2eJK2KCbQkSZKmRvX8oC0e3V4FnA3c0Mp3Axe0zzvbMm39OUkGPTVGkpZlAi1JkqSpkuSoJPcAh4Bbgb8GnmhPh4HeU2G2tc/bgIcB2vrD9ObvkaQVW9E90JIkSdK4VdXTwClJtgA3Ai8fVK29D7raPPAJMfPz8+sT4AitLubN6x7HgrXsw2nc/0uZpfbMUltg6fbMzc0tua0JtCRJkqZSVT2R5EvAGcCWJJvaVebtwIFWbT+wA9ifZBNwLPDYoO9bruM8aebn51cX895H1j+YZrX7cNVtmVCz1J5ZagusvT0m0JKkdbPl6pV0yjavqBP3xMXblq8kaeYl+WngH1ry/DzgNfQmBrsdeAO9mbh3ATe1Tfa05f+3rf9iVQ28Ai1JyzGBliRJ0jTZCuxOchS9+Xyur6qbk3wLuDbJHwBfB65s9a8EPpVkH70rzxeNI2hJs8EEWpIkSVOjqu4FTh1Q/iBw+oDyvwMuHEFokjYAZ+GWJEmSJKkDE2hJkiRJkjowgZYkSZIkqQMTaEmSJEmSOjCBliRJkiSpAxNoSZIkSZI6MIGWJEmSJKkDE2hJkiRJkjowgZYkSZIkqQMTaEmSJEmSOjCBliRJkiSpAxNoSZIkSZI6MIGWJEmSJKkDE2hJkiRJkjowgZYkSZIkqQMTaEmSJEmSOjCBliRJkiSpAxNoSZIkTY0kO5LcnuSBJPcneWcrf3+SR5Lc017n923z3iT7kvxVkl8aX/SSpp0JtCSNSJKrkhxKcl9fmR0+SVqZp4B3V9XLgTOAS5O8oq27vKpOaa9bANq6i4CfBc4F/q8kR40jcEnTzwRakkbnk/Q6b0eywydJHVXVwaq6u31+EngA2LbEJjuBa6vqR1X1HWAfcPrwI5U0i0ygJWlEqurLwGMdq9vhk6RlJDkJOBW4sxW9I8m9bcTPca1sG/Bw32b7WTrhlqRFbRp3AJIk3pHkLcDX6A1LfJxe5+6Ovjp2+CSpT5IXAJ8D3lVV309yBfD7QLX3DwNvAzJg8xr0nfPz80OKdnhWF/PmdY9jwVr24TTu/6XMUntmqS2wdHvm5uaW3NYEWpLGa80dPljJH7bhdZqGbdh/vCepczApsUxKHGAsi+mPZblO3yxJcjS95PnTVfV5gKp6tG/9x4Gb2+J+YEff5tuBA4O+d9r24fz8/Opi3vvI+gfTrHYfrrotE2qW2jNLbYG1t8cEWpLGaD06fLCCDssQO03DNsw/3pPUOZiUWCYlDjCWxUxSLKOUJMCVwANV9ZG+8q1VdbAt/gqwMGHjHuAzST4CvASYA74ywpAlzZBl74FO8twkX0nyjfaogN9t5ScnuTPJfJLrkjynlR/Tlve19ScNtwmSNL2SbO1bPLLDd1E7p56MHT5JWnAm8Gbg7COeYPAfk3wzyb3ALwD/HqCq7geuB74F/AVwaVU9PabYJU25LlegfwScXVU/aMNl9ib5c+A36c0ce22SPwUuoTcU8RLg8ap6WZKLgA8Bvzqk+CVpaiT5LHAW8OIk+4H3AWclOYXe8OyHgF+HXocvyUKH7yns8EkSAFW1l8G3udyyxDYfAD4wtKAkbRjLJtBVVcAP2uLR7VXA2cD/1Mp3A++nl0DvbJ8BbgD+OEna90jShlVVbxpQfOUS9e3wSZIkTZBOj7FKclSSe4BDwK3AXwNPVNVTrUr/7LA/eVRAW38YeNF6Bi1JkiRJ0qh1mkSsDRs8JckW4Ebg5YOqtfd1mzl2dLNcDndW2slo4/jYxtkwzDZuxElwJEmSNH1WNAt3VT2R5EvAGcCWJJvaVeb+2WEXZo7dn2QTcCzw2GLfuVjHeaQzSw55VtqJaOOY2MbZsBHaKEmSJC2nyyzcP92uPJPkecBrgAeA24E3tGq7gJva5z1tmbb+i97/LEmSJEmadl2uQG8Fdic5il7CfX1V3ZzkW8C1Sf4A+Dr/OBHOlcCnkuyjd+X5oiHELUmSJEnSSHWZhfte4NQB5Q8Cpw8o/zvgwnWJTpIkSZKkCdFpFm5JkiRJkjY6E2hJkiRJkjowgZYkSZIkqQMTaEmSJEmSOjCBliRJkiSpAxNoSZIkSZI6MIGWJEmSJKkDE2hJkiRJkjowgZYkSZIkqQMTaEmSJEmSOjCBliRJkiSpg03jDkCSpC62XP3IEL99M0/MDfHrJa2bJDuAa4CfAX4M/FlVfTTJ8cB1wEnAQ8Abq+rxJAE+CpwP/BB4a1XdPY7YJU0/r0BLkiRpmjwFvLuqXg6cAVya5BXAZcBtVTUH3NaWAc4D5trr7cAVow9Z0qwwgZYkSdLUqKqDC1eQq+pJ4AFgG7AT2N2q7QYuaJ93AtdUzx3AliRbRxy2pBlhAi1JkqSplOQk4FTgTuDEqjoIvSQbOKFV2wY83LfZ/lYmSSvmPdCSJEmaOkleAHwOeFdVfb93q/PgqgPKalDF+fn5dYpudFYX8+Z1j2PBWvbhNO7/pcxSe2apLbB0e+bmlp4UxQRakiRJUyXJ0fSS509X1edb8aNJtlbVwTZE+1Ar3w/s6Nt8O3Bg0Pcu13GeNPPz86uLee/wJmVc7T5cdVsm1Cy1Z5baAmtvj0O4JUmSNDXarNpXAg9U1Uf6Vu0BdrXPu4Cb+srfkp4zgMMLQ70laaW8Ai1JkqRpcibwZuCbSe5pZb8NfBC4PsklwHeBC9u6W+g9wmofvcdYXTzacCXNEhNoSZIkTY2q2svg+5oBzhlQv4BLhxqUpA3DIdySJEmSJHVgAi1JkiRJUgcm0JIkSZIkdWACLUmSJElSBybQkiRJkiR1YAItSZIkSVIHJtCSJEmSJHVgAi1JkiRJUgcm0JIkSZIkdWACLUkjkuSqJIeS3NdXdnySW5PMt/fjWnmSfCzJviT3JjltfJFLkiQJTKAlaZQ+CZx7RNllwG1VNQfc1pYBzgPm2uvtwBUjilGSJEmLMIGWpBGpqi8Djx1RvBPY3T7vBi7oK7+meu4AtiTZOppIJUmSNIgJtCSN14lVdRCgvZ/QyrcBD/fV29/KJEmSNCablquQZAdwDfAzwI+BP6uqjyY5HrgOOAl4CHhjVT2eJMBHgfOBHwJvraq7hxO+JM2sDCirxSrPz893/NrNq4tmA+i+D4dvUmKZlDjAWBbTH8vc3NwYI5GkjWHZBBp4Cnh3Vd2d5IXAXUluBd5K7769Dya5jN59e+/hmfftvZrefXuvHkbwkjQDHk2ytaoOtiHah1r5fmBHX73twIHFvqRzx3nvI6sMc/ZNSvIxPz8/EbFMShxgLIuZpFgkaaNYdgh3VR1cuIJcVU8CD9AbRuh9e5K0dnuAXe3zLuCmvvK3tNm4zwAOLwz1liRJ0nh0uQL9E0lOAk4F7uSI+/aSLHffnh0/SRtaks8CZwEvTrIfeB/wQeD6JJcA3wUubNVvoXcrzD56t8NcPPKAJUmS9AydE+gkLwA+B7yrqr7fu9V5cNUBZau6b2909xgN957AyWjj+NjG2TDMNm6UIYhV9aZFVp0zoG4Blw43IkmaPkmuAl4HHKqqn2tl7wf+DfC3rdpvV9Utbd17gUuAp4H/par+cuRBS5oZnRLoJEfTS54/XVWfb8VDvW9vpPf1DPmewIlo45jYxtmwEdooSZoanwT+mN4kt/0ur6o/7C9I8grgIuBngZcA/znJP62qp0cRqKTZs+w90G1W7SuBB6rqI32rvG9PkiRJI1VVXwYe61h9J3BtVf2oqr5D77aY04cWnKSZ1+U50GcCbwbOTnJPe51P7769X0wyD/xiW4befXsP0jtBfRz4d+sftiRJkvQM70hyb5KrkhzXyhabm0eSVmXZIdxVtZfB9zWD9+1JkiRp/K4Afp/evDu/D3wYeBvrODfPpFpdzMOb/2ct+3Aa9/9SZqk9s9QWWLo9y922uKJZuCVJkqRJU1WPLnxO8nHg5ra4LnPzTKpVz1EyxPl/VrsPZ22+lVlqzyy1Bdbeni5DuCVJkqSJ1Sa0XfArwH3t8x7goiTHJDkZmAO+Mur4JM0Or0BLkiRpaiT5LHAW8OIk+4H3AWclOYXe8OyHgF8HqKr7k1wPfAt4CrjUGbglrYUJtCRJkqZGVb1pQPGVS9T/APCB4UUkaSMxgZYkSZImyJaru96jvHmo9zNLejbvgZYkSZIkqQOvQEuSNALDvKL0xMU+1laSpFHwCrQkSZIkSR2YQEuSJEmS1IEJtCRJkiRJHZhAS5IkSZLUgQm0JEmSJEkdmEBLkiRJktSBCbQkSZIkSR2YQEuSJEmS1IEJtCRJkiRJHWwadwCSJEmSZsOWqx9Z5ZabYe/y2z5x8bZVfr+0PrwCLUmSJElSBybQkiRJkiR1MDVDuFc/HESSJEmSpLXzCrQkSZIkSR2YQEuSJEmS1IEJtCRJkqZGkquSHEpyX1/Z8UluTTLf3o9r5UnysST7ktyb5LTxRS5pFphAS5IkaZp8Ejj3iLLLgNuqag64rS0DnAfMtdfbgStGFKOkGWUCLUmSpKlRVV8GHjuieCewu33eDVzQV35N9dwBbEmydTSRSppFUzMLtyRJkrSIE6vqIEBVHUxyQivfBjzcV29/Kzs46Evm5+eHGmR3m8cdwMSanGO0vGmKdTmz1BZYuj1zc3NLbmsCLUmSpFmVAWW1WOXlOs4js9fHty5mYo7RMubn56cm1uXMUltg7e1xCLckSZKm3aMLQ7Pb+6FWvh/Y0VdvO3BgxLFJmiEm0JIkSZp2e4Bd7fMu4Ka+8re02bjPAA4vDPWWpNVwCLckSZKmRpLPAmcBL06yH3gf8EHg+iSXAN8FLmzVbwHOB/YBPwQuHnnAkmaKCbQkSZKmRlW9aZFV5wyoW8Clw41I0kZiAi1JEyDJQ8CTwNPAU1X1qiTHA9cBJwEPAW+sqsfHFaMkSdJG5z3QkjQ5fqGqTqmqV7Xly4DbqmoOuK0tS5IkaUyWvQKd5CrgdcChqvq5VjbwqkiSAB+ld6/JD4G3VtXdwwldkmbeTnr3+QHsBr4EvGdcwWhybbl6GI+82fyTR+k8cfG2IXy/JEnTp8sQ7k8Cfwxc01e2cFXkg0kua8vvAc4D5trr1cAV7V2StLQCvpCkgP+7qv4MOHFhttiqOpjkhMU2np+f7/hjNq890hk1nCR0NnT//ZrtGBZMaiyz9JxWSZpUyybQVfXlJCcdUbzYVZGdwDVtwoY7kmxJstXHBUjSss6sqgMtSb41ybdXsnHnjvNek0St3LgTs/n5+bHHsMBYJGljW+090M+4KgIsXBXZBjzcV29/K5MkLaGqDrT3Q8CNwOnAo0m2ArT3Q+OLUJIkSes9C3cGlNVSGyw1DOqZ66Z32GH3Ns4m2zgbhtnGjX4FJcnzgX9SVU+2z68Ffg/YA+yi93zTXcBN44tSkiRJq02gH10Ymn3EVZH9wI6+etuBA0t90WId52cNS5riYYed2ziDbONs2AhtHLMTgRt78zCyCfhMVf1Fkq8C1ye5BPgucOEYY5QkSdrwVptAL3ZVZA/wjiTX0ps87LD3P0vS0qrqQeCVA8q/B5wz+ogkSZI0SJfHWH2W3oRhL06yH3gfvcR50FWRW+g9wmofvcdYXTyEmCVJkiRJGrkus3C/aZFVz7oq0mbfvnStQUmSJEmSNGlWOwu3JEmSJEkbigm0JEmSJEkdmEBLkiRJktSBCbQkSZIkSR2YQEuSJEmS1MFqnwMtSZIkbUhbrn5k3CFIGhMTaEmSJM2EJA8BTwJPA09V1auSHA9cB5wEPAS8saoeH1eMkqabQ7glSZI0S36hqk6pqle15cuA26pqDritLUvSqphAS5IkaZbtBHa3z7uBC8YYi6QpZwItSZKkWVHAF5LcleTtrezEqjoI0N5PGFt0kqae90BLkiRpVpxZVQeSnADcmuTbK9l4fn6+Y83NK49M66L7MRq/aYp1ObPUFli6PXNzc0tuawItSZKkmVBVB9r7oSQ3AqcDjybZWlUHk2wFDi22/XId55/Y6yzc49L5GI3Z/Pz81MS6nFlqC6y9PQ7hliRJ0tRL8vwkL1z4DLwWuA/YA+xq1XYBN40nQkmzwCvQI7D4swI3r8v/YD5x8bY1f4ckSdKUOxG4MQn0+rifqaq/SPJV4PoklwDfBS4cY4ySppwJtCRJkqZeVT0IvHJA+feAc0YfkaRZ5BBuSZIkSZI6MIGWJEmSJKkDh3BLkiRJmgqLzy20PpxbSMvxCrQkSZIkSR2YQEuSJEmS1IFDuCVJ0lgtPyRzbY99dEimJGm9eAVakiRJkqQOTKAlSZIkSerABFqSJEmSpA68B3oGDHM6f+8bkyRJkqQer0BLkiRJktSBCbQkSZIkSR04hFuSJC1pmLcKSZI0TbwCLUmSJElSBybQkiRJkiR1YAItSZIkSVIHJtCSJEmSJHXgJGIaq2FPTONzrCVJkiStl6El0EnOBT4KHAV8oqo+OKyfpeFZe4K7GfY6e6u0Wp5LJWntPJdKWi9DSaCTHAX8CfCLwH7gq0n2VNW3hvHzJGkWeS6V1sf6jnZ65n8MO9Jp8nku1Uqs3/li8EUkzxnTL1W1/l+a/Dzw/qr6pbb8XoCq+t8BDh8+vP4/VNJMOPbYYzPuGCaF51JJq+W59B95LpW0WoPOpcOaRGwb8HDf8v5WJknqznOpJK2d51JJ62ZYCfSg//X0f/ckaWU8l0rS2nkulbRuhjWJ2H5gR9/yduDAwoLDiiSpE8+lkrR2nkslrZthXYH+KjCX5OQkzwEuAvYM6WdJ0qzyXCpJa+e5VNK6GUoCXVVPAe8A/hJ4ALi+qu7vsm2Sc5P8VZJ9SS4bRnyjkGRHktuTPJDk/iTvbOXHJ7k1yXx7P66VJ8nHWrvvTXLaeFvQXZKjknw9yc1t+eQkd7Y2Xtf+WJHkmLa8r60/aZxxd5VkS5Ibkny7Hc+fn7XjmOTft9/T+5J8NslzZ+04TqNhnktHdRwXOxceUeesJIeT3NNevzOMWNrPeijJN9vP+dqA9UP/N5zkn/W19Z4k30/yriPqDG2fJLkqyaEk9/WVDTynDdh2V6szn2TXkGL5P9r59t4kNybZssi2Sx7LdYrl/Uke6TsO5y+y7br2XRaJ5bq+OB5Kcs8i267rfpkFazmXTorFzqVd/+1OonTsP06DrKCvOA2ygn7hJFrJ37lV/d2vqol50Xs2318DLwWeA3wDeMW441plW7YCp7XPLwT+C/AK4D8Cl7Xyy4APtc/nA39O7z6dM4A7x92GFbT1N4HPADe35euBi9rnPwX+bfv874A/bZ8vAq4bd+wd27cb+J/b5+cAW2bpONKbSOU7wPP6jt9bZ+04bqRXl3PpqI7jYufCI+qctXD+GMG+eQh48RLrR/pvuB2rvwH+u1HtE+BfA6cB9/WVDTynHbHd8cCD7f249vm4IcTyWmBT+/yhQbF0OZbrFMv7gf+1wzFc177LoFiOWP9h4HdGsV98TcZrsXNpl3+7k/qiY/9xGl6soK846S9W2C+cxNdK/s6t5u/+sIZwr9bpwL6qerCq/h64Ftg55phWpaoOVtXd7fOT9P7Hcxu99uxu1XYDF7TPO4FrqucOYEuSrSMOe8WSbAd+GfhEWw5wNnBDq3JkGxfafgNwTqs/sZL8FL1/hFcCVNXfV9UTzNhxpDcfwvOSbAI2AweZoeO4AXU5l47kOC5xLpxUo/43fA7w11X1/w3xZzxDVX0ZeOyI4sXOaf1+Cbi1qh6rqseBW4Fz1zuWqvpC9a4YAtxB737VoVtkv3Sx7n2XpWJp/07fCHx2LT9D02UV/cqJtsL+40RbRV9xGqykXzhxVvh3bsV/9yctgZ7JxwykNzTyVOBO4MSqOgi9kyFwQqs2rW3/I+C3gB+35RcBT/R1fvrb8ZM2tvWHW/1J9lLgb4Gr2zCjTyR5PjN0HKvqEeAPge/SO0EeBu5ito7jRtPl93Dkx/GIc+GRfj7JN5L8eZKfHWIYBXwhyV1J3j5g/aj/DV/E4onQqPYJLH5O6zeO89vb6F0ZGGS5Y7le3tGG9V21yBDMUe+X/wF4tKrmF1k/qv2iMenYr5x0K+k/TrqV9hUn2ir6hdNi3fruk5ZAz9xjBpK8APgc8K6q+v5SVQeUTXTbk7wOOFRVd/UXD6haHdZNqk30hoBcUVWnAv+N3rCPxUxdG1uHcCdwMvAS4PnAeQOqTvNx3Gi6HKORHsdlzoV30xvC/Erg/wT+07DiAM6sqtPo/Y5fmuRfHxnqgG2Gsl/a/WOvB/6fAatHuU+6GvXvzH8AngI+vUiV5Y7lergC+O+BU+h1JD88KNQBZcM8J76Jpa8+j2K/aExW0K+cWKvoP066lfYVJ9oq+oXTbsW/e5OWQC/5mIFpk+Roeie5T1fV51vxowvDAtr7oVY+jW0/E3h9kofoDVk7m97/KG5pQz7gme34SRvb+mNZ3XC5UdoP7K+qhStmN9A7Sc7ScXwN8J2q+tuq+gfg88C/ZLaO40bT5fdwZMdxkXPhT1TV96vqB+3zLcDRSV48jFiq6kB7PwTcSG/4bb9R/hs+D7i7qh4dEOfI9kmz2Dmt38j2TXoTlL0O+LVqN6kdqcOxXLOqerSqnq6qHwMfX+RnjHK/bAL+R+C6xeqMYr9oPFbYr5xkK+0/TrqV9hUn3Ur7hdNi3fruk5ZAz8xjBtq9HFcCD1TVR/pW7QEWZi7dBdzUV/6WNhPcGcDhhWEGk6qq3ltV26vqJHrH6otV9WvA7cAbWrUj27jQ9je0+hP9v1dV9TfAw0n+WSs6B/gWM3Qc6Q3ROSPJ5vZ7u9DGmTmOG1CXc+lIjuMS58L+Oj+zcP91ktPp/W363hBieX6SFy58pjdZ1X1HVBvlv+FFrySOap/0Weyc1u8vgdcmOa5doXhtK1tXSc4F3gO8vqp+uEidLsdyPWLpvw/uVxb5GaPsu7wG+HZV7R+0clT7RaO3in7lxFpF/3GiraKvOOlW2i+cFuvXd68JmCmt/0VvJrT/Qm9Gy/8w7njW0I5/Re/y/73APe11Pr17PG4D5tv78a1+gD9p7f4m8Kpxt2GF7T2Lf5xF8aXAV4B99IYmHtPKn9uW97X1Lx133B3bdgrwtXYs/xO92Wdn6jgCvwt8m15H61PAMbN2HDfaa9C5FPg9eknJyI7jEufC3wB+o9V5B3A/vdmL7wD+5ZBieWn7Gd9oP29hv/THMpJ/w/QmZfkecGxf2Uj2Cb2k/SDwD/T+5/2SJc5prwI+0bft29rvzD7g4iHFso/e/WgLvy8Ls8W/BLhlqWM5hFg+1X4P7qXXydp6ZCxteV37LoNiaeWfXPgd6as71P3iazJeS5xLB/7bnZYXHfqP0/BiBX3FaXixgn7hJL5W+HduxX/30zaUJEmSJElLmLQh3JIkSZIkTSQTaEmSJEmSOjCBliRJkiSpAxNoSZIkSZI6MIGWJEmSJKkDE2hJkiRJkjowgZYkSZIkqQMTaEmSJEmSOvj/Af1UH0m11xfXAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 1080x1080 with 9 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "impute = Imputer(strategy='mean')\n",
    "# we will want to fill in missing values to see all 9 columns\n",
    "pima_imputed_mean = pd.DataFrame(impute.fit_transform(pima), columns=pima_column_names)\n",
    "pima_imputed_mean.hist(figsize=(15, 15))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>times_pregnant</th>\n",
       "      <th>plasma_glucose_concentration</th>\n",
       "      <th>diastolic_blood_pressure</th>\n",
       "      <th>triceps_thickness</th>\n",
       "      <th>serum_insulin</th>\n",
       "      <th>bmi</th>\n",
       "      <th>pedigree_function</th>\n",
       "      <th>age</th>\n",
       "      <th>onset_diabetes</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>768.000000</td>\n",
       "      <td>768.000000</td>\n",
       "      <td>768.000000</td>\n",
       "      <td>768.000000</td>\n",
       "      <td>768.000000</td>\n",
       "      <td>768.000000</td>\n",
       "      <td>768.000000</td>\n",
       "      <td>768.000000</td>\n",
       "      <td>768.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>3.845052</td>\n",
       "      <td>121.686763</td>\n",
       "      <td>72.405184</td>\n",
       "      <td>29.153420</td>\n",
       "      <td>155.548223</td>\n",
       "      <td>32.457464</td>\n",
       "      <td>0.471876</td>\n",
       "      <td>33.240885</td>\n",
       "      <td>0.348958</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>3.369578</td>\n",
       "      <td>30.435949</td>\n",
       "      <td>12.096346</td>\n",
       "      <td>8.790942</td>\n",
       "      <td>85.021108</td>\n",
       "      <td>6.875151</td>\n",
       "      <td>0.331329</td>\n",
       "      <td>11.760232</td>\n",
       "      <td>0.476951</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>44.000000</td>\n",
       "      <td>24.000000</td>\n",
       "      <td>7.000000</td>\n",
       "      <td>14.000000</td>\n",
       "      <td>18.200000</td>\n",
       "      <td>0.078000</td>\n",
       "      <td>21.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>99.750000</td>\n",
       "      <td>64.000000</td>\n",
       "      <td>25.000000</td>\n",
       "      <td>121.500000</td>\n",
       "      <td>27.500000</td>\n",
       "      <td>0.243750</td>\n",
       "      <td>24.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>3.000000</td>\n",
       "      <td>117.000000</td>\n",
       "      <td>72.202592</td>\n",
       "      <td>29.153420</td>\n",
       "      <td>155.548223</td>\n",
       "      <td>32.400000</td>\n",
       "      <td>0.372500</td>\n",
       "      <td>29.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>6.000000</td>\n",
       "      <td>140.250000</td>\n",
       "      <td>80.000000</td>\n",
       "      <td>32.000000</td>\n",
       "      <td>155.548223</td>\n",
       "      <td>36.600000</td>\n",
       "      <td>0.626250</td>\n",
       "      <td>41.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>17.000000</td>\n",
       "      <td>199.000000</td>\n",
       "      <td>122.000000</td>\n",
       "      <td>99.000000</td>\n",
       "      <td>846.000000</td>\n",
       "      <td>67.100000</td>\n",
       "      <td>2.420000</td>\n",
       "      <td>81.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       times_pregnant  plasma_glucose_concentration  diastolic_blood_pressure  \\\n",
       "count      768.000000                    768.000000                768.000000   \n",
       "mean         3.845052                    121.686763                 72.405184   \n",
       "std          3.369578                     30.435949                 12.096346   \n",
       "min          0.000000                     44.000000                 24.000000   \n",
       "25%          1.000000                     99.750000                 64.000000   \n",
       "50%          3.000000                    117.000000                 72.202592   \n",
       "75%          6.000000                    140.250000                 80.000000   \n",
       "max         17.000000                    199.000000                122.000000   \n",
       "\n",
       "       triceps_thickness  serum_insulin         bmi  pedigree_function  \\\n",
       "count         768.000000     768.000000  768.000000         768.000000   \n",
       "mean           29.153420     155.548223   32.457464           0.471876   \n",
       "std             8.790942      85.021108    6.875151           0.331329   \n",
       "min             7.000000      14.000000   18.200000           0.078000   \n",
       "25%            25.000000     121.500000   27.500000           0.243750   \n",
       "50%            29.153420     155.548223   32.400000           0.372500   \n",
       "75%            32.000000     155.548223   36.600000           0.626250   \n",
       "max            99.000000     846.000000   67.100000           2.420000   \n",
       "\n",
       "              age  onset_diabetes  \n",
       "count  768.000000      768.000000  \n",
       "mean    33.240885        0.348958  \n",
       "std     11.760232        0.476951  \n",
       "min     21.000000        0.000000  \n",
       "25%     24.000000        0.000000  \n",
       "50%     29.000000        0.000000  \n",
       "75%     41.000000        1.000000  \n",
       "max     81.000000        1.000000  "
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pima_imputed_mean.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[<matplotlib.axes._subplots.AxesSubplot object at 0x1a1b3ef278>,\n",
       "        <matplotlib.axes._subplots.AxesSubplot object at 0x1a1c548c88>,\n",
       "        <matplotlib.axes._subplots.AxesSubplot object at 0x1a1b3b0c50>],\n",
       "       [<matplotlib.axes._subplots.AxesSubplot object at 0x1a1caeb4e0>,\n",
       "        <matplotlib.axes._subplots.AxesSubplot object at 0x1a1b441d30>,\n",
       "        <matplotlib.axes._subplots.AxesSubplot object at 0x1a1ce645c0>],\n",
       "       [<matplotlib.axes._subplots.AxesSubplot object at 0x1a1ce91e10>,\n",
       "        <matplotlib.axes._subplots.AxesSubplot object at 0x1a1cecb6d8>,\n",
       "        <matplotlib.axes._subplots.AxesSubplot object at 0x1a1cecb710>]],\n",
       "      dtype=object)"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x1080 with 9 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "pima_imputed_mean.hist(figsize=(15, 15), sharex=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    0.864545\n",
       "1   -1.205376\n",
       "2    2.014501\n",
       "3   -1.073952\n",
       "4    0.503130\n",
       "Name: plasma_glucose_concentration, dtype: float64"
      ]
     },
     "execution_count": 55,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pima['plasma_glucose_concentration'].head()\n",
    "\n",
    "# get the mean of the column\n",
    "mu = pima['plasma_glucose_concentration'].mean()\n",
    "\n",
    "# get the standard deviation of the column\n",
    "sigma = pima['plasma_glucose_concentration'].std()\n",
    "\n",
    "# calculate z scores for every value in the column.\n",
    "((pima['plasma_glucose_concentration'] - mu) / sigma).head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'Distribution of plasma_glucose_concentration')"
      ]
     },
     "execution_count": 56,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "from sklearn.preprocessing import Normalizer, StandardScaler, MinMaxScaler\n",
    "pima['plasma_glucose_concentration'].mean(), pima['plasma_glucose_concentration'].std()\n",
    "ax = pima['plasma_glucose_concentration'].hist()\n",
    "ax.set_title('Distribution of plasma_glucose_concentration')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'Distribution of plasma_glucose_concentration after Z Score Scaling')"
      ]
     },
     "execution_count": 57,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "glucose_z_score_standardized = StandardScaler().fit_transform(pima[['plasma_glucose_concentration']])\n",
    "glucose_z_score_standardized.mean(), glucose_z_score_standardized.std()\n",
    "ax = pd.Series(glucose_z_score_standardized.reshape(-1,)).hist()\n",
    "ax.set_title('Distribution of plasma_glucose_concentration after Z Score Scaling')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'Distribution of plasma_glucose_concentration after Min Max Scaling')"
      ]
     },
     "execution_count": 58,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "glucose_min_max_standardized = MinMaxScaler().fit_transform(pima[['plasma_glucose_concentration']])\n",
    "glucose_min_max_standardized.mean(), glucose_min_max_standardized.std()\n",
    "ax = pd.Series(glucose_min_max_standardized.reshape(-1,)).hist()\n",
    "ax.set_title('Distribution of plasma_glucose_concentration after Min Max Scaling')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[<matplotlib.axes._subplots.AxesSubplot object at 0x1a1d6ae668>,\n",
       "        <matplotlib.axes._subplots.AxesSubplot object at 0x1a1d6c7f60>,\n",
       "        <matplotlib.axes._subplots.AxesSubplot object at 0x1a1d7907f0>],\n",
       "       [<matplotlib.axes._subplots.AxesSubplot object at 0x1a1d7cf080>,\n",
       "        <matplotlib.axes._subplots.AxesSubplot object at 0x1a1d7f98d0>,\n",
       "        <matplotlib.axes._subplots.AxesSubplot object at 0x1a1d837160>],\n",
       "       [<matplotlib.axes._subplots.AxesSubplot object at 0x1a1d8669b0>,\n",
       "        <matplotlib.axes._subplots.AxesSubplot object at 0x1a1d8a1278>,\n",
       "        <matplotlib.axes._subplots.AxesSubplot object at 0x1a1d8a12b0>]],\n",
       "      dtype=object)"
      ]
     },
     "execution_count": 59,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x1080 with 9 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "scale = StandardScaler()  # instantiate a z-scaler object\n",
    "\n",
    "pima_imputed_mean_scaled = pd.DataFrame(scale.fit_transform(pima_imputed_mean), columns=pima_column_names)\n",
    "\n",
    "pima_imputed_mean_scaled.hist(figsize=(15, 15), sharex=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/super/anaconda3/lib/python3.7/site-packages/sklearn/utils/deprecation.py:66: DeprecationWarning: Class Imputer is deprecated; Imputer was deprecated in version 0.20 and will be removed in 0.22. Import impute.SimpleImputer from sklearn instead.\n",
      "  warnings.warn(msg, category=DeprecationWarning)\n",
      "/Users/super/anaconda3/lib/python3.7/site-packages/sklearn/model_selection/_split.py:1978: FutureWarning: The default value of cv will change from 3 to 5 in version 0.22. Specify it explicitly to silence this warning.\n",
      "  warnings.warn(CV_WARNING, FutureWarning)\n",
      "/Users/super/anaconda3/lib/python3.7/site-packages/sklearn/utils/deprecation.py:66: DeprecationWarning: Class Imputer is deprecated; Imputer was deprecated in version 0.20 and will be removed in 0.22. Import impute.SimpleImputer from sklearn instead.\n",
      "  warnings.warn(msg, category=DeprecationWarning)\n",
      "/Users/super/anaconda3/lib/python3.7/site-packages/sklearn/utils/deprecation.py:66: DeprecationWarning: Class Imputer is deprecated; Imputer was deprecated in version 0.20 and will be removed in 0.22. Import impute.SimpleImputer from sklearn instead.\n",
      "  warnings.warn(msg, category=DeprecationWarning)\n",
      "/Users/super/anaconda3/lib/python3.7/site-packages/sklearn/utils/deprecation.py:66: DeprecationWarning: Class Imputer is deprecated; Imputer was deprecated in version 0.20 and will be removed in 0.22. Import impute.SimpleImputer from sklearn instead.\n",
      "  warnings.warn(msg, category=DeprecationWarning)\n",
      "/Users/super/anaconda3/lib/python3.7/site-packages/sklearn/utils/deprecation.py:66: DeprecationWarning: Class Imputer is deprecated; Imputer was deprecated in version 0.20 and will be removed in 0.22. Import impute.SimpleImputer from sklearn instead.\n",
      "  warnings.warn(msg, category=DeprecationWarning)\n",
      "/Users/super/anaconda3/lib/python3.7/site-packages/sklearn/utils/deprecation.py:66: DeprecationWarning: Class Imputer is deprecated; Imputer was deprecated in version 0.20 and will be removed in 0.22. Import impute.SimpleImputer from sklearn instead.\n",
      "  warnings.warn(msg, category=DeprecationWarning)\n",
      "/Users/super/anaconda3/lib/python3.7/site-packages/sklearn/utils/deprecation.py:66: DeprecationWarning: Class Imputer is deprecated; Imputer was deprecated in version 0.20 and will be removed in 0.22. Import impute.SimpleImputer from sklearn instead.\n",
      "  warnings.warn(msg, category=DeprecationWarning)\n",
      "/Users/super/anaconda3/lib/python3.7/site-packages/sklearn/utils/deprecation.py:66: DeprecationWarning: Class Imputer is deprecated; Imputer was deprecated in version 0.20 and will be removed in 0.22. Import impute.SimpleImputer from sklearn instead.\n",
      "  warnings.warn(msg, category=DeprecationWarning)\n",
      "/Users/super/anaconda3/lib/python3.7/site-packages/sklearn/utils/deprecation.py:66: DeprecationWarning: Class Imputer is deprecated; Imputer was deprecated in version 0.20 and will be removed in 0.22. Import impute.SimpleImputer from sklearn instead.\n",
      "  warnings.warn(msg, category=DeprecationWarning)\n",
      "/Users/super/anaconda3/lib/python3.7/site-packages/sklearn/utils/deprecation.py:66: DeprecationWarning: Class Imputer is deprecated; Imputer was deprecated in version 0.20 and will be removed in 0.22. Import impute.SimpleImputer from sklearn instead.\n",
      "  warnings.warn(msg, category=DeprecationWarning)\n",
      "/Users/super/anaconda3/lib/python3.7/site-packages/sklearn/utils/deprecation.py:66: DeprecationWarning: Class Imputer is deprecated; Imputer was deprecated in version 0.20 and will be removed in 0.22. Import impute.SimpleImputer from sklearn instead.\n",
      "  warnings.warn(msg, category=DeprecationWarning)\n",
      "/Users/super/anaconda3/lib/python3.7/site-packages/sklearn/utils/deprecation.py:66: DeprecationWarning: Class Imputer is deprecated; Imputer was deprecated in version 0.20 and will be removed in 0.22. Import impute.SimpleImputer from sklearn instead.\n",
      "  warnings.warn(msg, category=DeprecationWarning)\n",
      "/Users/super/anaconda3/lib/python3.7/site-packages/sklearn/utils/deprecation.py:66: DeprecationWarning: Class Imputer is deprecated; Imputer was deprecated in version 0.20 and will be removed in 0.22. Import impute.SimpleImputer from sklearn instead.\n",
      "  warnings.warn(msg, category=DeprecationWarning)\n",
      "/Users/super/anaconda3/lib/python3.7/site-packages/sklearn/utils/deprecation.py:66: DeprecationWarning: Class Imputer is deprecated; Imputer was deprecated in version 0.20 and will be removed in 0.22. Import impute.SimpleImputer from sklearn instead.\n",
      "  warnings.warn(msg, category=DeprecationWarning)\n",
      "/Users/super/anaconda3/lib/python3.7/site-packages/sklearn/utils/deprecation.py:66: DeprecationWarning: Class Imputer is deprecated; Imputer was deprecated in version 0.20 and will be removed in 0.22. Import impute.SimpleImputer from sklearn instead.\n",
      "  warnings.warn(msg, category=DeprecationWarning)\n",
      "/Users/super/anaconda3/lib/python3.7/site-packages/sklearn/utils/deprecation.py:66: DeprecationWarning: Class Imputer is deprecated; Imputer was deprecated in version 0.20 and will be removed in 0.22. Import impute.SimpleImputer from sklearn instead.\n",
      "  warnings.warn(msg, category=DeprecationWarning)\n",
      "/Users/super/anaconda3/lib/python3.7/site-packages/sklearn/utils/deprecation.py:66: DeprecationWarning: Class Imputer is deprecated; Imputer was deprecated in version 0.20 and will be removed in 0.22. Import impute.SimpleImputer from sklearn instead.\n",
      "  warnings.warn(msg, category=DeprecationWarning)\n",
      "/Users/super/anaconda3/lib/python3.7/site-packages/sklearn/utils/deprecation.py:66: DeprecationWarning: Class Imputer is deprecated; Imputer was deprecated in version 0.20 and will be removed in 0.22. Import impute.SimpleImputer from sklearn instead.\n",
      "  warnings.warn(msg, category=DeprecationWarning)\n",
      "/Users/super/anaconda3/lib/python3.7/site-packages/sklearn/utils/deprecation.py:66: DeprecationWarning: Class Imputer is deprecated; Imputer was deprecated in version 0.20 and will be removed in 0.22. Import impute.SimpleImputer from sklearn instead.\n",
      "  warnings.warn(msg, category=DeprecationWarning)\n",
      "/Users/super/anaconda3/lib/python3.7/site-packages/sklearn/utils/deprecation.py:66: DeprecationWarning: Class Imputer is deprecated; Imputer was deprecated in version 0.20 and will be removed in 0.22. Import impute.SimpleImputer from sklearn instead.\n",
      "  warnings.warn(msg, category=DeprecationWarning)\n",
      "/Users/super/anaconda3/lib/python3.7/site-packages/sklearn/utils/deprecation.py:66: DeprecationWarning: Class Imputer is deprecated; Imputer was deprecated in version 0.20 and will be removed in 0.22. Import impute.SimpleImputer from sklearn instead.\n",
      "  warnings.warn(msg, category=DeprecationWarning)\n",
      "/Users/super/anaconda3/lib/python3.7/site-packages/sklearn/utils/deprecation.py:66: DeprecationWarning: Class Imputer is deprecated; Imputer was deprecated in version 0.20 and will be removed in 0.22. Import impute.SimpleImputer from sklearn instead.\n",
      "  warnings.warn(msg, category=DeprecationWarning)\n",
      "/Users/super/anaconda3/lib/python3.7/site-packages/sklearn/utils/deprecation.py:66: DeprecationWarning: Class Imputer is deprecated; Imputer was deprecated in version 0.20 and will be removed in 0.22. Import impute.SimpleImputer from sklearn instead.\n",
      "  warnings.warn(msg, category=DeprecationWarning)\n",
      "/Users/super/anaconda3/lib/python3.7/site-packages/sklearn/utils/deprecation.py:66: DeprecationWarning: Class Imputer is deprecated; Imputer was deprecated in version 0.20 and will be removed in 0.22. Import impute.SimpleImputer from sklearn instead.\n",
      "  warnings.warn(msg, category=DeprecationWarning)\n",
      "/Users/super/anaconda3/lib/python3.7/site-packages/sklearn/utils/deprecation.py:66: DeprecationWarning: Class Imputer is deprecated; Imputer was deprecated in version 0.20 and will be removed in 0.22. Import impute.SimpleImputer from sklearn instead.\n",
      "  warnings.warn(msg, category=DeprecationWarning)\n",
      "/Users/super/anaconda3/lib/python3.7/site-packages/sklearn/utils/deprecation.py:66: DeprecationWarning: Class Imputer is deprecated; Imputer was deprecated in version 0.20 and will be removed in 0.22. Import impute.SimpleImputer from sklearn instead.\n",
      "  warnings.warn(msg, category=DeprecationWarning)\n",
      "/Users/super/anaconda3/lib/python3.7/site-packages/sklearn/utils/deprecation.py:66: DeprecationWarning: Class Imputer is deprecated; Imputer was deprecated in version 0.20 and will be removed in 0.22. Import impute.SimpleImputer from sklearn instead.\n",
      "  warnings.warn(msg, category=DeprecationWarning)\n",
      "/Users/super/anaconda3/lib/python3.7/site-packages/sklearn/utils/deprecation.py:66: DeprecationWarning: Class Imputer is deprecated; Imputer was deprecated in version 0.20 and will be removed in 0.22. Import impute.SimpleImputer from sklearn instead.\n",
      "  warnings.warn(msg, category=DeprecationWarning)\n",
      "/Users/super/anaconda3/lib/python3.7/site-packages/sklearn/utils/deprecation.py:66: DeprecationWarning: Class Imputer is deprecated; Imputer was deprecated in version 0.20 and will be removed in 0.22. Import impute.SimpleImputer from sklearn instead.\n",
      "  warnings.warn(msg, category=DeprecationWarning)\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/super/anaconda3/lib/python3.7/site-packages/sklearn/utils/deprecation.py:66: DeprecationWarning: Class Imputer is deprecated; Imputer was deprecated in version 0.20 and will be removed in 0.22. Import impute.SimpleImputer from sklearn instead.\n",
      "  warnings.warn(msg, category=DeprecationWarning)\n",
      "/Users/super/anaconda3/lib/python3.7/site-packages/sklearn/utils/deprecation.py:66: DeprecationWarning: Class Imputer is deprecated; Imputer was deprecated in version 0.20 and will be removed in 0.22. Import impute.SimpleImputer from sklearn instead.\n",
      "  warnings.warn(msg, category=DeprecationWarning)\n",
      "/Users/super/anaconda3/lib/python3.7/site-packages/sklearn/utils/deprecation.py:66: DeprecationWarning: Class Imputer is deprecated; Imputer was deprecated in version 0.20 and will be removed in 0.22. Import impute.SimpleImputer from sklearn instead.\n",
      "  warnings.warn(msg, category=DeprecationWarning)\n",
      "/Users/super/anaconda3/lib/python3.7/site-packages/sklearn/utils/deprecation.py:66: DeprecationWarning: Class Imputer is deprecated; Imputer was deprecated in version 0.20 and will be removed in 0.22. Import impute.SimpleImputer from sklearn instead.\n",
      "  warnings.warn(msg, category=DeprecationWarning)\n",
      "/Users/super/anaconda3/lib/python3.7/site-packages/sklearn/utils/deprecation.py:66: DeprecationWarning: Class Imputer is deprecated; Imputer was deprecated in version 0.20 and will be removed in 0.22. Import impute.SimpleImputer from sklearn instead.\n",
      "  warnings.warn(msg, category=DeprecationWarning)\n",
      "/Users/super/anaconda3/lib/python3.7/site-packages/sklearn/utils/deprecation.py:66: DeprecationWarning: Class Imputer is deprecated; Imputer was deprecated in version 0.20 and will be removed in 0.22. Import impute.SimpleImputer from sklearn instead.\n",
      "  warnings.warn(msg, category=DeprecationWarning)\n",
      "/Users/super/anaconda3/lib/python3.7/site-packages/sklearn/utils/deprecation.py:66: DeprecationWarning: Class Imputer is deprecated; Imputer was deprecated in version 0.20 and will be removed in 0.22. Import impute.SimpleImputer from sklearn instead.\n",
      "  warnings.warn(msg, category=DeprecationWarning)\n",
      "/Users/super/anaconda3/lib/python3.7/site-packages/sklearn/utils/deprecation.py:66: DeprecationWarning: Class Imputer is deprecated; Imputer was deprecated in version 0.20 and will be removed in 0.22. Import impute.SimpleImputer from sklearn instead.\n",
      "  warnings.warn(msg, category=DeprecationWarning)\n",
      "/Users/super/anaconda3/lib/python3.7/site-packages/sklearn/utils/deprecation.py:66: DeprecationWarning: Class Imputer is deprecated; Imputer was deprecated in version 0.20 and will be removed in 0.22. Import impute.SimpleImputer from sklearn instead.\n",
      "  warnings.warn(msg, category=DeprecationWarning)\n",
      "/Users/super/anaconda3/lib/python3.7/site-packages/sklearn/utils/deprecation.py:66: DeprecationWarning: Class Imputer is deprecated; Imputer was deprecated in version 0.20 and will be removed in 0.22. Import impute.SimpleImputer from sklearn instead.\n",
      "  warnings.warn(msg, category=DeprecationWarning)\n",
      "/Users/super/anaconda3/lib/python3.7/site-packages/sklearn/utils/deprecation.py:66: DeprecationWarning: Class Imputer is deprecated; Imputer was deprecated in version 0.20 and will be removed in 0.22. Import impute.SimpleImputer from sklearn instead.\n",
      "  warnings.warn(msg, category=DeprecationWarning)\n",
      "/Users/super/anaconda3/lib/python3.7/site-packages/sklearn/utils/deprecation.py:66: DeprecationWarning: Class Imputer is deprecated; Imputer was deprecated in version 0.20 and will be removed in 0.22. Import impute.SimpleImputer from sklearn instead.\n",
      "  warnings.warn(msg, category=DeprecationWarning)\n",
      "/Users/super/anaconda3/lib/python3.7/site-packages/sklearn/utils/deprecation.py:66: DeprecationWarning: Class Imputer is deprecated; Imputer was deprecated in version 0.20 and will be removed in 0.22. Import impute.SimpleImputer from sklearn instead.\n",
      "  warnings.warn(msg, category=DeprecationWarning)\n",
      "/Users/super/anaconda3/lib/python3.7/site-packages/sklearn/utils/deprecation.py:66: DeprecationWarning: Class Imputer is deprecated; Imputer was deprecated in version 0.20 and will be removed in 0.22. Import impute.SimpleImputer from sklearn instead.\n",
      "  warnings.warn(msg, category=DeprecationWarning)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.7421875 {'classify__n_neighbors': 7, 'imputer__strategy': 'median'}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/super/anaconda3/lib/python3.7/site-packages/sklearn/utils/deprecation.py:66: DeprecationWarning: Class Imputer is deprecated; Imputer was deprecated in version 0.20 and will be removed in 0.22. Import impute.SimpleImputer from sklearn instead.\n",
      "  warnings.warn(msg, category=DeprecationWarning)\n",
      "/Users/super/anaconda3/lib/python3.7/site-packages/sklearn/utils/deprecation.py:66: DeprecationWarning: Class Imputer is deprecated; Imputer was deprecated in version 0.20 and will be removed in 0.22. Import impute.SimpleImputer from sklearn instead.\n",
      "  warnings.warn(msg, category=DeprecationWarning)\n"
     ]
    }
   ],
   "source": [
    "knn_params = {'imputer__strategy':['mean', 'median'], 'classify__n_neighbors':[1, 2, 3, 4, 5, 6, 7]}\n",
    "\n",
    "mean_impute_standardize = Pipeline([('imputer', Imputer()), ('standardize', StandardScaler()), ('classify', knn)])\n",
    "X = pima.drop('onset_diabetes', axis=1)\n",
    "y = pima['onset_diabetes']\n",
    "\n",
    "grid = GridSearchCV(mean_impute_standardize, knn_params)\n",
    "grid.fit(X, y)\n",
    "\n",
    "print(grid.best_score_, grid.best_params_)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>times_pregnant</th>\n",
       "      <th>plasma_glucose_concentration</th>\n",
       "      <th>diastolic_blood_pressure</th>\n",
       "      <th>triceps_thickness</th>\n",
       "      <th>serum_insulin</th>\n",
       "      <th>bmi</th>\n",
       "      <th>pedigree_function</th>\n",
       "      <th>age</th>\n",
       "      <th>onset_diabetes</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>768.000000</td>\n",
       "      <td>768.000000</td>\n",
       "      <td>768.000000</td>\n",
       "      <td>768.000000</td>\n",
       "      <td>768.000000</td>\n",
       "      <td>768.000000</td>\n",
       "      <td>768.000000</td>\n",
       "      <td>768.000000</td>\n",
       "      <td>768.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>0.226180</td>\n",
       "      <td>0.501205</td>\n",
       "      <td>0.493930</td>\n",
       "      <td>0.240798</td>\n",
       "      <td>0.170130</td>\n",
       "      <td>0.291564</td>\n",
       "      <td>0.168179</td>\n",
       "      <td>0.204015</td>\n",
       "      <td>0.348958</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>0.198210</td>\n",
       "      <td>0.196361</td>\n",
       "      <td>0.123432</td>\n",
       "      <td>0.095554</td>\n",
       "      <td>0.102189</td>\n",
       "      <td>0.140596</td>\n",
       "      <td>0.141473</td>\n",
       "      <td>0.196004</td>\n",
       "      <td>0.476951</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>0.058824</td>\n",
       "      <td>0.359677</td>\n",
       "      <td>0.408163</td>\n",
       "      <td>0.195652</td>\n",
       "      <td>0.129207</td>\n",
       "      <td>0.190184</td>\n",
       "      <td>0.070773</td>\n",
       "      <td>0.050000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>0.176471</td>\n",
       "      <td>0.470968</td>\n",
       "      <td>0.491863</td>\n",
       "      <td>0.240798</td>\n",
       "      <td>0.170130</td>\n",
       "      <td>0.290389</td>\n",
       "      <td>0.125747</td>\n",
       "      <td>0.133333</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>0.352941</td>\n",
       "      <td>0.620968</td>\n",
       "      <td>0.571429</td>\n",
       "      <td>0.271739</td>\n",
       "      <td>0.170130</td>\n",
       "      <td>0.376278</td>\n",
       "      <td>0.234095</td>\n",
       "      <td>0.333333</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       times_pregnant  plasma_glucose_concentration  diastolic_blood_pressure  \\\n",
       "count      768.000000                    768.000000                768.000000   \n",
       "mean         0.226180                      0.501205                  0.493930   \n",
       "std          0.198210                      0.196361                  0.123432   \n",
       "min          0.000000                      0.000000                  0.000000   \n",
       "25%          0.058824                      0.359677                  0.408163   \n",
       "50%          0.176471                      0.470968                  0.491863   \n",
       "75%          0.352941                      0.620968                  0.571429   \n",
       "max          1.000000                      1.000000                  1.000000   \n",
       "\n",
       "       triceps_thickness  serum_insulin         bmi  pedigree_function  \\\n",
       "count         768.000000     768.000000  768.000000         768.000000   \n",
       "mean            0.240798       0.170130    0.291564           0.168179   \n",
       "std             0.095554       0.102189    0.140596           0.141473   \n",
       "min             0.000000       0.000000    0.000000           0.000000   \n",
       "25%             0.195652       0.129207    0.190184           0.070773   \n",
       "50%             0.240798       0.170130    0.290389           0.125747   \n",
       "75%             0.271739       0.170130    0.376278           0.234095   \n",
       "max             1.000000       1.000000    1.000000           1.000000   \n",
       "\n",
       "              age  onset_diabetes  \n",
       "count  768.000000      768.000000  \n",
       "mean     0.204015        0.348958  \n",
       "std      0.196004        0.476951  \n",
       "min      0.000000        0.000000  \n",
       "25%      0.050000        0.000000  \n",
       "50%      0.133333        0.000000  \n",
       "75%      0.333333        1.000000  \n",
       "max      1.000000        1.000000  "
      ]
     },
     "execution_count": 62,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.preprocessing import MinMaxScaler\n",
    "min_max = MinMaxScaler()\n",
    "pima_min_maxed = pd.DataFrame(min_max.fit_transform(pima_imputed), columns=pima_column_names)\n",
    "pima_min_maxed.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/super/anaconda3/lib/python3.7/site-packages/sklearn/utils/deprecation.py:66: DeprecationWarning: Class Imputer is deprecated; Imputer was deprecated in version 0.20 and will be removed in 0.22. Import impute.SimpleImputer from sklearn instead.\n",
      "  warnings.warn(msg, category=DeprecationWarning)\n",
      "/Users/super/anaconda3/lib/python3.7/site-packages/sklearn/model_selection/_split.py:1978: FutureWarning: The default value of cv will change from 3 to 5 in version 0.22. Specify it explicitly to silence this warning.\n",
      "  warnings.warn(CV_WARNING, FutureWarning)\n",
      "/Users/super/anaconda3/lib/python3.7/site-packages/sklearn/utils/deprecation.py:66: DeprecationWarning: Class Imputer is deprecated; Imputer was deprecated in version 0.20 and will be removed in 0.22. Import impute.SimpleImputer from sklearn instead.\n",
      "  warnings.warn(msg, category=DeprecationWarning)\n",
      "/Users/super/anaconda3/lib/python3.7/site-packages/sklearn/utils/deprecation.py:66: DeprecationWarning: Class Imputer is deprecated; Imputer was deprecated in version 0.20 and will be removed in 0.22. Import impute.SimpleImputer from sklearn instead.\n",
      "  warnings.warn(msg, category=DeprecationWarning)\n",
      "/Users/super/anaconda3/lib/python3.7/site-packages/sklearn/utils/deprecation.py:66: DeprecationWarning: Class Imputer is deprecated; Imputer was deprecated in version 0.20 and will be removed in 0.22. Import impute.SimpleImputer from sklearn instead.\n",
      "  warnings.warn(msg, category=DeprecationWarning)\n",
      "/Users/super/anaconda3/lib/python3.7/site-packages/sklearn/utils/deprecation.py:66: DeprecationWarning: Class Imputer is deprecated; Imputer was deprecated in version 0.20 and will be removed in 0.22. Import impute.SimpleImputer from sklearn instead.\n",
      "  warnings.warn(msg, category=DeprecationWarning)\n",
      "/Users/super/anaconda3/lib/python3.7/site-packages/sklearn/utils/deprecation.py:66: DeprecationWarning: Class Imputer is deprecated; Imputer was deprecated in version 0.20 and will be removed in 0.22. Import impute.SimpleImputer from sklearn instead.\n",
      "  warnings.warn(msg, category=DeprecationWarning)\n",
      "/Users/super/anaconda3/lib/python3.7/site-packages/sklearn/utils/deprecation.py:66: DeprecationWarning: Class Imputer is deprecated; Imputer was deprecated in version 0.20 and will be removed in 0.22. Import impute.SimpleImputer from sklearn instead.\n",
      "  warnings.warn(msg, category=DeprecationWarning)\n",
      "/Users/super/anaconda3/lib/python3.7/site-packages/sklearn/utils/deprecation.py:66: DeprecationWarning: Class Imputer is deprecated; Imputer was deprecated in version 0.20 and will be removed in 0.22. Import impute.SimpleImputer from sklearn instead.\n",
      "  warnings.warn(msg, category=DeprecationWarning)\n",
      "/Users/super/anaconda3/lib/python3.7/site-packages/sklearn/utils/deprecation.py:66: DeprecationWarning: Class Imputer is deprecated; Imputer was deprecated in version 0.20 and will be removed in 0.22. Import impute.SimpleImputer from sklearn instead.\n",
      "  warnings.warn(msg, category=DeprecationWarning)\n",
      "/Users/super/anaconda3/lib/python3.7/site-packages/sklearn/utils/deprecation.py:66: DeprecationWarning: Class Imputer is deprecated; Imputer was deprecated in version 0.20 and will be removed in 0.22. Import impute.SimpleImputer from sklearn instead.\n",
      "  warnings.warn(msg, category=DeprecationWarning)\n",
      "/Users/super/anaconda3/lib/python3.7/site-packages/sklearn/utils/deprecation.py:66: DeprecationWarning: Class Imputer is deprecated; Imputer was deprecated in version 0.20 and will be removed in 0.22. Import impute.SimpleImputer from sklearn instead.\n",
      "  warnings.warn(msg, category=DeprecationWarning)\n",
      "/Users/super/anaconda3/lib/python3.7/site-packages/sklearn/utils/deprecation.py:66: DeprecationWarning: Class Imputer is deprecated; Imputer was deprecated in version 0.20 and will be removed in 0.22. Import impute.SimpleImputer from sklearn instead.\n",
      "  warnings.warn(msg, category=DeprecationWarning)\n",
      "/Users/super/anaconda3/lib/python3.7/site-packages/sklearn/utils/deprecation.py:66: DeprecationWarning: Class Imputer is deprecated; Imputer was deprecated in version 0.20 and will be removed in 0.22. Import impute.SimpleImputer from sklearn instead.\n",
      "  warnings.warn(msg, category=DeprecationWarning)\n",
      "/Users/super/anaconda3/lib/python3.7/site-packages/sklearn/utils/deprecation.py:66: DeprecationWarning: Class Imputer is deprecated; Imputer was deprecated in version 0.20 and will be removed in 0.22. Import impute.SimpleImputer from sklearn instead.\n",
      "  warnings.warn(msg, category=DeprecationWarning)\n",
      "/Users/super/anaconda3/lib/python3.7/site-packages/sklearn/utils/deprecation.py:66: DeprecationWarning: Class Imputer is deprecated; Imputer was deprecated in version 0.20 and will be removed in 0.22. Import impute.SimpleImputer from sklearn instead.\n",
      "  warnings.warn(msg, category=DeprecationWarning)\n",
      "/Users/super/anaconda3/lib/python3.7/site-packages/sklearn/utils/deprecation.py:66: DeprecationWarning: Class Imputer is deprecated; Imputer was deprecated in version 0.20 and will be removed in 0.22. Import impute.SimpleImputer from sklearn instead.\n",
      "  warnings.warn(msg, category=DeprecationWarning)\n",
      "/Users/super/anaconda3/lib/python3.7/site-packages/sklearn/utils/deprecation.py:66: DeprecationWarning: Class Imputer is deprecated; Imputer was deprecated in version 0.20 and will be removed in 0.22. Import impute.SimpleImputer from sklearn instead.\n",
      "  warnings.warn(msg, category=DeprecationWarning)\n",
      "/Users/super/anaconda3/lib/python3.7/site-packages/sklearn/utils/deprecation.py:66: DeprecationWarning: Class Imputer is deprecated; Imputer was deprecated in version 0.20 and will be removed in 0.22. Import impute.SimpleImputer from sklearn instead.\n",
      "  warnings.warn(msg, category=DeprecationWarning)\n",
      "/Users/super/anaconda3/lib/python3.7/site-packages/sklearn/utils/deprecation.py:66: DeprecationWarning: Class Imputer is deprecated; Imputer was deprecated in version 0.20 and will be removed in 0.22. Import impute.SimpleImputer from sklearn instead.\n",
      "  warnings.warn(msg, category=DeprecationWarning)\n",
      "/Users/super/anaconda3/lib/python3.7/site-packages/sklearn/utils/deprecation.py:66: DeprecationWarning: Class Imputer is deprecated; Imputer was deprecated in version 0.20 and will be removed in 0.22. Import impute.SimpleImputer from sklearn instead.\n",
      "  warnings.warn(msg, category=DeprecationWarning)\n",
      "/Users/super/anaconda3/lib/python3.7/site-packages/sklearn/utils/deprecation.py:66: DeprecationWarning: Class Imputer is deprecated; Imputer was deprecated in version 0.20 and will be removed in 0.22. Import impute.SimpleImputer from sklearn instead.\n",
      "  warnings.warn(msg, category=DeprecationWarning)\n",
      "/Users/super/anaconda3/lib/python3.7/site-packages/sklearn/utils/deprecation.py:66: DeprecationWarning: Class Imputer is deprecated; Imputer was deprecated in version 0.20 and will be removed in 0.22. Import impute.SimpleImputer from sklearn instead.\n",
      "  warnings.warn(msg, category=DeprecationWarning)\n",
      "/Users/super/anaconda3/lib/python3.7/site-packages/sklearn/utils/deprecation.py:66: DeprecationWarning: Class Imputer is deprecated; Imputer was deprecated in version 0.20 and will be removed in 0.22. Import impute.SimpleImputer from sklearn instead.\n",
      "  warnings.warn(msg, category=DeprecationWarning)\n",
      "/Users/super/anaconda3/lib/python3.7/site-packages/sklearn/utils/deprecation.py:66: DeprecationWarning: Class Imputer is deprecated; Imputer was deprecated in version 0.20 and will be removed in 0.22. Import impute.SimpleImputer from sklearn instead.\n",
      "  warnings.warn(msg, category=DeprecationWarning)\n",
      "/Users/super/anaconda3/lib/python3.7/site-packages/sklearn/utils/deprecation.py:66: DeprecationWarning: Class Imputer is deprecated; Imputer was deprecated in version 0.20 and will be removed in 0.22. Import impute.SimpleImputer from sklearn instead.\n",
      "  warnings.warn(msg, category=DeprecationWarning)\n",
      "/Users/super/anaconda3/lib/python3.7/site-packages/sklearn/utils/deprecation.py:66: DeprecationWarning: Class Imputer is deprecated; Imputer was deprecated in version 0.20 and will be removed in 0.22. Import impute.SimpleImputer from sklearn instead.\n",
      "  warnings.warn(msg, category=DeprecationWarning)\n",
      "/Users/super/anaconda3/lib/python3.7/site-packages/sklearn/utils/deprecation.py:66: DeprecationWarning: Class Imputer is deprecated; Imputer was deprecated in version 0.20 and will be removed in 0.22. Import impute.SimpleImputer from sklearn instead.\n",
      "  warnings.warn(msg, category=DeprecationWarning)\n",
      "/Users/super/anaconda3/lib/python3.7/site-packages/sklearn/utils/deprecation.py:66: DeprecationWarning: Class Imputer is deprecated; Imputer was deprecated in version 0.20 and will be removed in 0.22. Import impute.SimpleImputer from sklearn instead.\n",
      "  warnings.warn(msg, category=DeprecationWarning)\n",
      "/Users/super/anaconda3/lib/python3.7/site-packages/sklearn/utils/deprecation.py:66: DeprecationWarning: Class Imputer is deprecated; Imputer was deprecated in version 0.20 and will be removed in 0.22. Import impute.SimpleImputer from sklearn instead.\n",
      "  warnings.warn(msg, category=DeprecationWarning)\n",
      "/Users/super/anaconda3/lib/python3.7/site-packages/sklearn/utils/deprecation.py:66: DeprecationWarning: Class Imputer is deprecated; Imputer was deprecated in version 0.20 and will be removed in 0.22. Import impute.SimpleImputer from sklearn instead.\n",
      "  warnings.warn(msg, category=DeprecationWarning)\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/super/anaconda3/lib/python3.7/site-packages/sklearn/utils/deprecation.py:66: DeprecationWarning: Class Imputer is deprecated; Imputer was deprecated in version 0.20 and will be removed in 0.22. Import impute.SimpleImputer from sklearn instead.\n",
      "  warnings.warn(msg, category=DeprecationWarning)\n",
      "/Users/super/anaconda3/lib/python3.7/site-packages/sklearn/utils/deprecation.py:66: DeprecationWarning: Class Imputer is deprecated; Imputer was deprecated in version 0.20 and will be removed in 0.22. Import impute.SimpleImputer from sklearn instead.\n",
      "  warnings.warn(msg, category=DeprecationWarning)\n",
      "/Users/super/anaconda3/lib/python3.7/site-packages/sklearn/utils/deprecation.py:66: DeprecationWarning: Class Imputer is deprecated; Imputer was deprecated in version 0.20 and will be removed in 0.22. Import impute.SimpleImputer from sklearn instead.\n",
      "  warnings.warn(msg, category=DeprecationWarning)\n",
      "/Users/super/anaconda3/lib/python3.7/site-packages/sklearn/utils/deprecation.py:66: DeprecationWarning: Class Imputer is deprecated; Imputer was deprecated in version 0.20 and will be removed in 0.22. Import impute.SimpleImputer from sklearn instead.\n",
      "  warnings.warn(msg, category=DeprecationWarning)\n",
      "/Users/super/anaconda3/lib/python3.7/site-packages/sklearn/utils/deprecation.py:66: DeprecationWarning: Class Imputer is deprecated; Imputer was deprecated in version 0.20 and will be removed in 0.22. Import impute.SimpleImputer from sklearn instead.\n",
      "  warnings.warn(msg, category=DeprecationWarning)\n",
      "/Users/super/anaconda3/lib/python3.7/site-packages/sklearn/utils/deprecation.py:66: DeprecationWarning: Class Imputer is deprecated; Imputer was deprecated in version 0.20 and will be removed in 0.22. Import impute.SimpleImputer from sklearn instead.\n",
      "  warnings.warn(msg, category=DeprecationWarning)\n",
      "/Users/super/anaconda3/lib/python3.7/site-packages/sklearn/utils/deprecation.py:66: DeprecationWarning: Class Imputer is deprecated; Imputer was deprecated in version 0.20 and will be removed in 0.22. Import impute.SimpleImputer from sklearn instead.\n",
      "  warnings.warn(msg, category=DeprecationWarning)\n",
      "/Users/super/anaconda3/lib/python3.7/site-packages/sklearn/utils/deprecation.py:66: DeprecationWarning: Class Imputer is deprecated; Imputer was deprecated in version 0.20 and will be removed in 0.22. Import impute.SimpleImputer from sklearn instead.\n",
      "  warnings.warn(msg, category=DeprecationWarning)\n",
      "/Users/super/anaconda3/lib/python3.7/site-packages/sklearn/utils/deprecation.py:66: DeprecationWarning: Class Imputer is deprecated; Imputer was deprecated in version 0.20 and will be removed in 0.22. Import impute.SimpleImputer from sklearn instead.\n",
      "  warnings.warn(msg, category=DeprecationWarning)\n",
      "/Users/super/anaconda3/lib/python3.7/site-packages/sklearn/utils/deprecation.py:66: DeprecationWarning: Class Imputer is deprecated; Imputer was deprecated in version 0.20 and will be removed in 0.22. Import impute.SimpleImputer from sklearn instead.\n",
      "  warnings.warn(msg, category=DeprecationWarning)\n",
      "/Users/super/anaconda3/lib/python3.7/site-packages/sklearn/utils/deprecation.py:66: DeprecationWarning: Class Imputer is deprecated; Imputer was deprecated in version 0.20 and will be removed in 0.22. Import impute.SimpleImputer from sklearn instead.\n",
      "  warnings.warn(msg, category=DeprecationWarning)\n",
      "/Users/super/anaconda3/lib/python3.7/site-packages/sklearn/utils/deprecation.py:66: DeprecationWarning: Class Imputer is deprecated; Imputer was deprecated in version 0.20 and will be removed in 0.22. Import impute.SimpleImputer from sklearn instead.\n",
      "  warnings.warn(msg, category=DeprecationWarning)\n",
      "/Users/super/anaconda3/lib/python3.7/site-packages/sklearn/utils/deprecation.py:66: DeprecationWarning: Class Imputer is deprecated; Imputer was deprecated in version 0.20 and will be removed in 0.22. Import impute.SimpleImputer from sklearn instead.\n",
      "  warnings.warn(msg, category=DeprecationWarning)\n",
      "/Users/super/anaconda3/lib/python3.7/site-packages/sklearn/utils/deprecation.py:66: DeprecationWarning: Class Imputer is deprecated; Imputer was deprecated in version 0.20 and will be removed in 0.22. Import impute.SimpleImputer from sklearn instead.\n",
      "  warnings.warn(msg, category=DeprecationWarning)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.74609375 {'classify__n_neighbors': 4, 'imputer__strategy': 'mean'}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/super/anaconda3/lib/python3.7/site-packages/sklearn/utils/deprecation.py:66: DeprecationWarning: Class Imputer is deprecated; Imputer was deprecated in version 0.20 and will be removed in 0.22. Import impute.SimpleImputer from sklearn instead.\n",
      "  warnings.warn(msg, category=DeprecationWarning)\n"
     ]
    }
   ],
   "source": [
    "knn_params = {'imputer__strategy': ['mean', 'median'], 'classify__n_neighbors':[1, 2, 3, 4, 5, 6, 7]}\n",
    "\n",
    "mean_impute_standardize = Pipeline([('imputer', Imputer()), ('standardize', MinMaxScaler()), ('classify', knn)])\n",
    "X = pima.drop('onset_diabetes', axis=1)\n",
    "y = pima['onset_diabetes']\n",
    "\n",
    "grid = GridSearchCV(mean_impute_standardize, knn_params)\n",
    "grid.fit(X, y)\n",
    "\n",
    "print(grid.best_score_, grid.best_params_)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1.0"
      ]
     },
     "execution_count": 64,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.preprocessing import Normalizer\n",
    "normalize = Normalizer()\n",
    "\n",
    "pima_normalized = pd.DataFrame(normalize.fit_transform(pima_imputed), columns=pima_column_names)\n",
    "np.sqrt((pima_normalized**2).sum(axis=1)).mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/super/anaconda3/lib/python3.7/site-packages/sklearn/utils/deprecation.py:66: DeprecationWarning: Class Imputer is deprecated; Imputer was deprecated in version 0.20 and will be removed in 0.22. Import impute.SimpleImputer from sklearn instead.\n",
      "  warnings.warn(msg, category=DeprecationWarning)\n",
      "/Users/super/anaconda3/lib/python3.7/site-packages/sklearn/model_selection/_split.py:1978: FutureWarning: The default value of cv will change from 3 to 5 in version 0.22. Specify it explicitly to silence this warning.\n",
      "  warnings.warn(CV_WARNING, FutureWarning)\n",
      "/Users/super/anaconda3/lib/python3.7/site-packages/sklearn/utils/deprecation.py:66: DeprecationWarning: Class Imputer is deprecated; Imputer was deprecated in version 0.20 and will be removed in 0.22. Import impute.SimpleImputer from sklearn instead.\n",
      "  warnings.warn(msg, category=DeprecationWarning)\n",
      "/Users/super/anaconda3/lib/python3.7/site-packages/sklearn/utils/deprecation.py:66: DeprecationWarning: Class Imputer is deprecated; Imputer was deprecated in version 0.20 and will be removed in 0.22. Import impute.SimpleImputer from sklearn instead.\n",
      "  warnings.warn(msg, category=DeprecationWarning)\n",
      "/Users/super/anaconda3/lib/python3.7/site-packages/sklearn/utils/deprecation.py:66: DeprecationWarning: Class Imputer is deprecated; Imputer was deprecated in version 0.20 and will be removed in 0.22. Import impute.SimpleImputer from sklearn instead.\n",
      "  warnings.warn(msg, category=DeprecationWarning)\n",
      "/Users/super/anaconda3/lib/python3.7/site-packages/sklearn/utils/deprecation.py:66: DeprecationWarning: Class Imputer is deprecated; Imputer was deprecated in version 0.20 and will be removed in 0.22. Import impute.SimpleImputer from sklearn instead.\n",
      "  warnings.warn(msg, category=DeprecationWarning)\n",
      "/Users/super/anaconda3/lib/python3.7/site-packages/sklearn/utils/deprecation.py:66: DeprecationWarning: Class Imputer is deprecated; Imputer was deprecated in version 0.20 and will be removed in 0.22. Import impute.SimpleImputer from sklearn instead.\n",
      "  warnings.warn(msg, category=DeprecationWarning)\n",
      "/Users/super/anaconda3/lib/python3.7/site-packages/sklearn/utils/deprecation.py:66: DeprecationWarning: Class Imputer is deprecated; Imputer was deprecated in version 0.20 and will be removed in 0.22. Import impute.SimpleImputer from sklearn instead.\n",
      "  warnings.warn(msg, category=DeprecationWarning)\n",
      "/Users/super/anaconda3/lib/python3.7/site-packages/sklearn/utils/deprecation.py:66: DeprecationWarning: Class Imputer is deprecated; Imputer was deprecated in version 0.20 and will be removed in 0.22. Import impute.SimpleImputer from sklearn instead.\n",
      "  warnings.warn(msg, category=DeprecationWarning)\n",
      "/Users/super/anaconda3/lib/python3.7/site-packages/sklearn/utils/deprecation.py:66: DeprecationWarning: Class Imputer is deprecated; Imputer was deprecated in version 0.20 and will be removed in 0.22. Import impute.SimpleImputer from sklearn instead.\n",
      "  warnings.warn(msg, category=DeprecationWarning)\n",
      "/Users/super/anaconda3/lib/python3.7/site-packages/sklearn/utils/deprecation.py:66: DeprecationWarning: Class Imputer is deprecated; Imputer was deprecated in version 0.20 and will be removed in 0.22. Import impute.SimpleImputer from sklearn instead.\n",
      "  warnings.warn(msg, category=DeprecationWarning)\n",
      "/Users/super/anaconda3/lib/python3.7/site-packages/sklearn/utils/deprecation.py:66: DeprecationWarning: Class Imputer is deprecated; Imputer was deprecated in version 0.20 and will be removed in 0.22. Import impute.SimpleImputer from sklearn instead.\n",
      "  warnings.warn(msg, category=DeprecationWarning)\n",
      "/Users/super/anaconda3/lib/python3.7/site-packages/sklearn/utils/deprecation.py:66: DeprecationWarning: Class Imputer is deprecated; Imputer was deprecated in version 0.20 and will be removed in 0.22. Import impute.SimpleImputer from sklearn instead.\n",
      "  warnings.warn(msg, category=DeprecationWarning)\n",
      "/Users/super/anaconda3/lib/python3.7/site-packages/sklearn/utils/deprecation.py:66: DeprecationWarning: Class Imputer is deprecated; Imputer was deprecated in version 0.20 and will be removed in 0.22. Import impute.SimpleImputer from sklearn instead.\n",
      "  warnings.warn(msg, category=DeprecationWarning)\n",
      "/Users/super/anaconda3/lib/python3.7/site-packages/sklearn/utils/deprecation.py:66: DeprecationWarning: Class Imputer is deprecated; Imputer was deprecated in version 0.20 and will be removed in 0.22. Import impute.SimpleImputer from sklearn instead.\n",
      "  warnings.warn(msg, category=DeprecationWarning)\n",
      "/Users/super/anaconda3/lib/python3.7/site-packages/sklearn/utils/deprecation.py:66: DeprecationWarning: Class Imputer is deprecated; Imputer was deprecated in version 0.20 and will be removed in 0.22. Import impute.SimpleImputer from sklearn instead.\n",
      "  warnings.warn(msg, category=DeprecationWarning)\n",
      "/Users/super/anaconda3/lib/python3.7/site-packages/sklearn/utils/deprecation.py:66: DeprecationWarning: Class Imputer is deprecated; Imputer was deprecated in version 0.20 and will be removed in 0.22. Import impute.SimpleImputer from sklearn instead.\n",
      "  warnings.warn(msg, category=DeprecationWarning)\n",
      "/Users/super/anaconda3/lib/python3.7/site-packages/sklearn/utils/deprecation.py:66: DeprecationWarning: Class Imputer is deprecated; Imputer was deprecated in version 0.20 and will be removed in 0.22. Import impute.SimpleImputer from sklearn instead.\n",
      "  warnings.warn(msg, category=DeprecationWarning)\n",
      "/Users/super/anaconda3/lib/python3.7/site-packages/sklearn/utils/deprecation.py:66: DeprecationWarning: Class Imputer is deprecated; Imputer was deprecated in version 0.20 and will be removed in 0.22. Import impute.SimpleImputer from sklearn instead.\n",
      "  warnings.warn(msg, category=DeprecationWarning)\n",
      "/Users/super/anaconda3/lib/python3.7/site-packages/sklearn/utils/deprecation.py:66: DeprecationWarning: Class Imputer is deprecated; Imputer was deprecated in version 0.20 and will be removed in 0.22. Import impute.SimpleImputer from sklearn instead.\n",
      "  warnings.warn(msg, category=DeprecationWarning)\n",
      "/Users/super/anaconda3/lib/python3.7/site-packages/sklearn/utils/deprecation.py:66: DeprecationWarning: Class Imputer is deprecated; Imputer was deprecated in version 0.20 and will be removed in 0.22. Import impute.SimpleImputer from sklearn instead.\n",
      "  warnings.warn(msg, category=DeprecationWarning)\n",
      "/Users/super/anaconda3/lib/python3.7/site-packages/sklearn/utils/deprecation.py:66: DeprecationWarning: Class Imputer is deprecated; Imputer was deprecated in version 0.20 and will be removed in 0.22. Import impute.SimpleImputer from sklearn instead.\n",
      "  warnings.warn(msg, category=DeprecationWarning)\n",
      "/Users/super/anaconda3/lib/python3.7/site-packages/sklearn/utils/deprecation.py:66: DeprecationWarning: Class Imputer is deprecated; Imputer was deprecated in version 0.20 and will be removed in 0.22. Import impute.SimpleImputer from sklearn instead.\n",
      "  warnings.warn(msg, category=DeprecationWarning)\n",
      "/Users/super/anaconda3/lib/python3.7/site-packages/sklearn/utils/deprecation.py:66: DeprecationWarning: Class Imputer is deprecated; Imputer was deprecated in version 0.20 and will be removed in 0.22. Import impute.SimpleImputer from sklearn instead.\n",
      "  warnings.warn(msg, category=DeprecationWarning)\n",
      "/Users/super/anaconda3/lib/python3.7/site-packages/sklearn/utils/deprecation.py:66: DeprecationWarning: Class Imputer is deprecated; Imputer was deprecated in version 0.20 and will be removed in 0.22. Import impute.SimpleImputer from sklearn instead.\n",
      "  warnings.warn(msg, category=DeprecationWarning)\n",
      "/Users/super/anaconda3/lib/python3.7/site-packages/sklearn/utils/deprecation.py:66: DeprecationWarning: Class Imputer is deprecated; Imputer was deprecated in version 0.20 and will be removed in 0.22. Import impute.SimpleImputer from sklearn instead.\n",
      "  warnings.warn(msg, category=DeprecationWarning)\n",
      "/Users/super/anaconda3/lib/python3.7/site-packages/sklearn/utils/deprecation.py:66: DeprecationWarning: Class Imputer is deprecated; Imputer was deprecated in version 0.20 and will be removed in 0.22. Import impute.SimpleImputer from sklearn instead.\n",
      "  warnings.warn(msg, category=DeprecationWarning)\n",
      "/Users/super/anaconda3/lib/python3.7/site-packages/sklearn/utils/deprecation.py:66: DeprecationWarning: Class Imputer is deprecated; Imputer was deprecated in version 0.20 and will be removed in 0.22. Import impute.SimpleImputer from sklearn instead.\n",
      "  warnings.warn(msg, category=DeprecationWarning)\n",
      "/Users/super/anaconda3/lib/python3.7/site-packages/sklearn/utils/deprecation.py:66: DeprecationWarning: Class Imputer is deprecated; Imputer was deprecated in version 0.20 and will be removed in 0.22. Import impute.SimpleImputer from sklearn instead.\n",
      "  warnings.warn(msg, category=DeprecationWarning)\n",
      "/Users/super/anaconda3/lib/python3.7/site-packages/sklearn/utils/deprecation.py:66: DeprecationWarning: Class Imputer is deprecated; Imputer was deprecated in version 0.20 and will be removed in 0.22. Import impute.SimpleImputer from sklearn instead.\n",
      "  warnings.warn(msg, category=DeprecationWarning)\n",
      "/Users/super/anaconda3/lib/python3.7/site-packages/sklearn/utils/deprecation.py:66: DeprecationWarning: Class Imputer is deprecated; Imputer was deprecated in version 0.20 and will be removed in 0.22. Import impute.SimpleImputer from sklearn instead.\n",
      "  warnings.warn(msg, category=DeprecationWarning)\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/super/anaconda3/lib/python3.7/site-packages/sklearn/utils/deprecation.py:66: DeprecationWarning: Class Imputer is deprecated; Imputer was deprecated in version 0.20 and will be removed in 0.22. Import impute.SimpleImputer from sklearn instead.\n",
      "  warnings.warn(msg, category=DeprecationWarning)\n",
      "/Users/super/anaconda3/lib/python3.7/site-packages/sklearn/utils/deprecation.py:66: DeprecationWarning: Class Imputer is deprecated; Imputer was deprecated in version 0.20 and will be removed in 0.22. Import impute.SimpleImputer from sklearn instead.\n",
      "  warnings.warn(msg, category=DeprecationWarning)\n",
      "/Users/super/anaconda3/lib/python3.7/site-packages/sklearn/utils/deprecation.py:66: DeprecationWarning: Class Imputer is deprecated; Imputer was deprecated in version 0.20 and will be removed in 0.22. Import impute.SimpleImputer from sklearn instead.\n",
      "  warnings.warn(msg, category=DeprecationWarning)\n",
      "/Users/super/anaconda3/lib/python3.7/site-packages/sklearn/utils/deprecation.py:66: DeprecationWarning: Class Imputer is deprecated; Imputer was deprecated in version 0.20 and will be removed in 0.22. Import impute.SimpleImputer from sklearn instead.\n",
      "  warnings.warn(msg, category=DeprecationWarning)\n",
      "/Users/super/anaconda3/lib/python3.7/site-packages/sklearn/utils/deprecation.py:66: DeprecationWarning: Class Imputer is deprecated; Imputer was deprecated in version 0.20 and will be removed in 0.22. Import impute.SimpleImputer from sklearn instead.\n",
      "  warnings.warn(msg, category=DeprecationWarning)\n",
      "/Users/super/anaconda3/lib/python3.7/site-packages/sklearn/utils/deprecation.py:66: DeprecationWarning: Class Imputer is deprecated; Imputer was deprecated in version 0.20 and will be removed in 0.22. Import impute.SimpleImputer from sklearn instead.\n",
      "  warnings.warn(msg, category=DeprecationWarning)\n",
      "/Users/super/anaconda3/lib/python3.7/site-packages/sklearn/utils/deprecation.py:66: DeprecationWarning: Class Imputer is deprecated; Imputer was deprecated in version 0.20 and will be removed in 0.22. Import impute.SimpleImputer from sklearn instead.\n",
      "  warnings.warn(msg, category=DeprecationWarning)\n",
      "/Users/super/anaconda3/lib/python3.7/site-packages/sklearn/utils/deprecation.py:66: DeprecationWarning: Class Imputer is deprecated; Imputer was deprecated in version 0.20 and will be removed in 0.22. Import impute.SimpleImputer from sklearn instead.\n",
      "  warnings.warn(msg, category=DeprecationWarning)\n",
      "/Users/super/anaconda3/lib/python3.7/site-packages/sklearn/utils/deprecation.py:66: DeprecationWarning: Class Imputer is deprecated; Imputer was deprecated in version 0.20 and will be removed in 0.22. Import impute.SimpleImputer from sklearn instead.\n",
      "  warnings.warn(msg, category=DeprecationWarning)\n",
      "/Users/super/anaconda3/lib/python3.7/site-packages/sklearn/utils/deprecation.py:66: DeprecationWarning: Class Imputer is deprecated; Imputer was deprecated in version 0.20 and will be removed in 0.22. Import impute.SimpleImputer from sklearn instead.\n",
      "  warnings.warn(msg, category=DeprecationWarning)\n",
      "/Users/super/anaconda3/lib/python3.7/site-packages/sklearn/utils/deprecation.py:66: DeprecationWarning: Class Imputer is deprecated; Imputer was deprecated in version 0.20 and will be removed in 0.22. Import impute.SimpleImputer from sklearn instead.\n",
      "  warnings.warn(msg, category=DeprecationWarning)\n",
      "/Users/super/anaconda3/lib/python3.7/site-packages/sklearn/utils/deprecation.py:66: DeprecationWarning: Class Imputer is deprecated; Imputer was deprecated in version 0.20 and will be removed in 0.22. Import impute.SimpleImputer from sklearn instead.\n",
      "  warnings.warn(msg, category=DeprecationWarning)\n",
      "/Users/super/anaconda3/lib/python3.7/site-packages/sklearn/utils/deprecation.py:66: DeprecationWarning: Class Imputer is deprecated; Imputer was deprecated in version 0.20 and will be removed in 0.22. Import impute.SimpleImputer from sklearn instead.\n",
      "  warnings.warn(msg, category=DeprecationWarning)\n",
      "/Users/super/anaconda3/lib/python3.7/site-packages/sklearn/utils/deprecation.py:66: DeprecationWarning: Class Imputer is deprecated; Imputer was deprecated in version 0.20 and will be removed in 0.22. Import impute.SimpleImputer from sklearn instead.\n",
      "  warnings.warn(msg, category=DeprecationWarning)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.6822916666666666 {'classify__n_neighbors': 6, 'imputer__strategy': 'mean'}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/super/anaconda3/lib/python3.7/site-packages/sklearn/utils/deprecation.py:66: DeprecationWarning: Class Imputer is deprecated; Imputer was deprecated in version 0.20 and will be removed in 0.22. Import impute.SimpleImputer from sklearn instead.\n",
      "  warnings.warn(msg, category=DeprecationWarning)\n"
     ]
    }
   ],
   "source": [
    "knn_params = {'imputer__strategy': ['mean', 'median'], 'classify__n_neighbors':[1, 2, 3, 4, 5, 6, 7]}\n",
    "\n",
    "mean_impute_normalize = Pipeline([('imputer', Imputer()), ('normalize', Normalizer()), ('classify', knn)])\n",
    "X = pima.drop('onset_diabetes', axis=1)\n",
    "y = pima['onset_diabetes']\n",
    "\n",
    "grid = GridSearchCV(mean_impute_normalize, knn_params)\n",
    "grid.fit(X, y)\n",
    "\n",
    "print(grid.best_score_, grid.best_params_)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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